Description:FIELD OF THE INVENTION

[0001] The present invention relates to a multi-factor authentication and cheating prevention system that is capable of performing advanced identity verification of examinees using multiple validation approaches to ensure accurate and reliable authentication before granting access to an examination environment.

BACKGROUND OF THE INVENTION

[0002] Ensuring secure identity verification and maintaining fairness in controlled environments has become increasingly critical with the rise of large-scale examinations and credential-based evaluations. Accurate verification processes help confirm that only authorized individuals gain access, while continuous oversight discourages dishonest practices. The approach plays a vital role in preserving integrity and trust in evaluation processes. In real-life scenarios, the approach supports transparent assessment in academic institutions, competitive testing centers, and professional certification settings, reducing impersonation risks and promoting equal opportunity by ensuring that every participant is evaluated under consistent and monitored conditions.

[0003] The conventional practices for identity verification and exam supervision rely heavily on manual checks and general observation, which lack consistency and accuracy. The approaches depend on human vigilance, making them prone to oversight, fatigue, and subjective judgment. They provide limited capability to detect subtle or coordinated dishonest behaviors, especially in large or crowded environments. Additionally, real-time tracking and analysis remain minimal, reducing the ability to respond promptly to irregular activities. The limitations create gaps in maintaining strict fairness, allowing instances of impersonation and malpractice to occur despite existing preventive measures.

[0004] US12238113B2 discloses a method of multi-factor authentication, the method comprising computer executed steps, the steps comprising: from a computer of a cloud service, receiving data identifying a user logged-in to the cloud service after being successfully authenticated using a first authentication factor, communicating with a client device of the logged-in user, for receiving a second authentication factor from the logged-in user, determining whether the second authentication factor received from the logged-in user is valid, based on a result of the determining, determining a first user-permission policy for the logged-in user, and communicating the determined first user-permission policy to the computer of the cloud service, for the cloud service to base a restriction of usage of the cloud service by the logged-in user on.

[0005] US8984605B2 discloses a method provides a designated link in a notification to an intended recipient of the message. The designated link includes a unique identifier associated with the message. Upon receiving a request to access the message, the method authenticates the request. Authentication includes verifying whether the request corresponds to the designated link provided in the notification. If the request passes authentication, the method communicates the message.

[0006] Conventionally, many means disclosed in the prior art provides ways for identifying verification and examination supervision that relies on manual checking, basic visual inspection, and isolated monitoring practices. However, the existing means limit accuracy, scalability, and real-time responsiveness. Moreover, behavioral tracking is minimal that introduces increased complexity in consistently detecting unfair practices and ensuring comprehensive monitoring in large examination environments.

[0007] In order to overcome the aforementioned drawbacks, there exists a need in the art to develop a system that requires to be capable of performing reliable multi-level identity verification of examinees using diverse validation measures, ensuring precise authentication prior to entry into examination settings. Additionally, the developed system is required to enhance accuracy, strengthen supervision, and maintain fairness and controlled conduct throughout assessment environments.

OBJECTS OF THE INVENTION

[0008] The principal object of the present invention is to overcome the disadvantages of the prior art.

[0009] An object of the present invention is to develop a system that is capable of verifying examinee identity through sequential validation stages and ensuring secure and reliable admission control at examination centers, thereby enhancing overall authentication accuracy and reducing unauthorized access.

[0010] Another object of the present invention is to develop a system that is capable of continuously monitoring examinee behavior and movement patterns within the examination environment and identifying abnormal or suspicious activities in real time, thereby improving supervision effectiveness and reducing the possibility of malpractice during examinations.

[0011] Yet, another object of the present invention is to develop a system that is capable of dynamically controlling and regulating examinee movement and examination environment conditions based on authentication status and monitored behavior, thereby ensuring structured navigation, and maintaining examination integrity.

[0012] The foregoing and other objects, features, and advantages of the present invention will become readily apparent upon further review of the following detailed description of the preferred embodiment as illustrated in the accompanying drawings.

SUMMARY OF THE INVENTION

[0013] The present invention relates to a multi-factor authentication and cheating prevention system that is capable of continuously monitoring examinee behavior during examinations to detect suspicious activities, irregular movement patterns, and unauthorized interactions, ensuring maintenance of fairness and integrity throughout the examination process.

[0014] According to an aspect of the present invention, a multi-factor authentication and cheating prevention system, comprises of a multi-factor biometric authentication assembly at an examination center entrance to perform individual examinee verification and generate an alert via an embedded speaker upon mismatch detection, a foot platform incorporating a plurality of pressure sensors for detecting user presence, a fingerprint scanner with a fingerprint sensor to scan the examinee’s fingerprint for retrieving individual pre-fed profiles to a linked database, an imaging unit with an integrated three-dimensional depth sensor and an infrared sensor to capturing facial images, enabling the AI module to utilize FER (facial expression recognition) protocols to match the examinee against the retrieved profile, enabling the microcontroller to trigger an alert upon mismatch detection, and an acoustic echo biometric verification module at the entrance of the examination center to be activated upon confirmed physical presence and capture ear canal geometry, cartilage structure and cavity dimensions, enabling an AI (artificial intelligence) module with a connected microcontroller to generate an individual Ear Resonance Profile for final identity confirmation.

[0015] According to another aspect of the present invention, the system further comprises of an interactive display panel at the examination center entrance to present the authenticated examinee's assigned room number, seat number and precise seat location, a storage compartment at the examination center entrance to store rough sheets and writing tools for the examinees, enabling a robotic gripper arm on a slider at the entrance to retrieve and present the rough sheets and writing tools to individual examinees upon identity confirmation, a plurality of color-coded LED (light emitting diode) strips throughout the examination center flooring to illuminate a unique color-coded route corresponding exclusively to each individual examinee’s assigned navigation path, a plurality of thermal sensors and microphones in each auxiliary examination desk to generate movement heat maps, and capture examinee conversations, enabling the connected AI module to utilize trained machine learning protocols for identifying suspicious movement and behavior and accordingly trigger an alert notification, and a plurality of cascading plates on a guide rail on each lateral side of the examination desk to deploy along the guide rails forming a barrier around the examinee to prevent malpractice attempts.

[0016] While the invention has been described and shown with particular reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0017] These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description, appended claims, and accompanying drawings where:
Figure 1 illustrates an isometric view of a multi-factor authentication and cheating prevention system; and
Figure 2 illustrates an inner view of an examination room associated with the system.

DETAILED DESCRIPTION OF THE INVENTION

[0018] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.

[0019] In any embodiment described herein, the open-ended terms "comprising," "comprises,” and the like (which are synonymous with "including," "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like.

[0020] As used herein, the singular forms “a,” “an,” and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.

[0021] The present invention relates to a multi-factor authentication and cheating prevention system that is capable of guiding and managing examinee flow within an examination facility by providing structured navigation, optimized seating allocation, and controlled positioning to ensure organized and efficient conduct of examination procedures.

[0022] Referring to Figure 1 and Figure 2, an isometric view of a multi-factor authentication and cheating prevention system and an inner view of an examination room associated with the system are illustrated, respectively, comprising a multi-factor biometric authentication assembly installed at an examination center entrance comprising a foot platform 101 incorporating a plurality of pressure sensors 102, a fingerprint scanner 103, an imaging unit 105 mounted on the examination center entrance, an embedded speaker 106 with the examination center, an interactive display panel 107 installed at the examination center entrance, a storage compartment 108 is installed at the examination center entrance, a robotic gripper arm 109 mounted on a slider 110 installed at the entrance, a plurality of color-coded LED (light emitting diode) strips 111 embedded throughout the examination center flooring, a plurality of linked surveillance cameras 104 positioned throughout the center, a plurality of thermal sensors 201 and microphones 202 embedded in each auxiliary examination desk 203, and a plurality of cascading plates 204 mounted on a guide rail 205 installed on each lateral side of the examination desk 203.

[0023] The system disclosed herein is designed for examination centers to ensure secure identity verification and malpractice prevention. The system comprises a multi-factor biometric authentication assembly that is installed at the examination center entrance to perform individual examinee verification. The multi-factor biometric authentication assembly includes a foot platform 101 incorporating a plurality of pressure sensors 102 for detecting user presence, and initiating the sequential authentication operations.

[0024] The pressure sensor 102 operates by converting the applied mechanical force of the weight of the user into a proportional electrical signal through a sensing element such as a piezoresistive element. When a user steps onto the foot platform 101, the sensing diaphragm deforms under body weight, resulting in a measurable change in electrical resistance depending on the internal configuration. The variation is converted into a voltage signal using a Wheatstone bridge circuit. The signal is then amplified, filtered, and digitized for processing by a signal convertor such as analog to digital convertor. The processed output is sent to the microcontroller that indicates user presence and initiates sequential authentication operations.

[0025] The multi-factor biometric authentication assembly further includes a fingerprint scanner 103 that captures the examinee’s fingerprint and retrieves the corresponding pre-stored individual profiles from a linked database by the microcontroller. The fingerprint scanner 103 captures the examinee’s fingerprint by converting ridge and valley patterns into a digital signal processed through an embedded signal conditioning unit, wherein the microcontroller performs pre-processing to enhance ridge clarity and segmentation to isolate the fingerprint region. A feature extraction module identifies minutiae points including ridge endings and bifurcations and converts them into a structured template encoded as a compact digital representation. The encoded template is transmitted to the microcontroller, which retrieves corresponding pre-stored individual profiles from the linked database. The matching operation compares the live template with stored templates using pattern correlation techniques to generate similarity scores, enabling authentication or mismatch detection based on a predefined threshold.

[0026] An imaging unit 105 with an integrated three-dimensional depth sensor and an infrared sensor captures the facial images of the user. The imaging unit 105 operates by capturing light from the facial region through an optical lens assembly and focusing it onto a photosensitive array. The optical lens assembly focuses incoming light from the user’s facial region onto the sensor array, where each pixel converts the incident light into corresponding electrical charges proportional to the intensity received. These analog signals are sequentially read out, amplified, and filtered to reduce noise and enhance signal quality. The processed signals are then converted into digital image data, which forms the primary facial image input for further processing by the microcontroller.

[0027] The integrated three-dimensional depth sensor operates within the imaging unit 105 by emitting structured light toward the user’s face and capturing the reflected patterns through an optical receiver array. The reflected signals are converted into electrical data and processed to determine phase shift, time-of-flight, and pattern deformation. The depth processing operation then computes distance values across multiple facial points to generate a depth map representing the spatial geometry of the face. The depth data is refined using filtering and noise reduction operations and continuously updated to enable accurate three-dimensional facial capture.

[0028] The infrared sensor operates within the imaging unit 105 by emitting infrared radiation corresponding to the user’s facial region and converting the detected energy into electrical signals. In active mode, emitted radiation reflects from the face and is captured to determine presence and proximity, while in passive mode, naturally emitted thermal radiation is detected. The received signals are amplified, filtered, and digitized for processing. The microcontroller analyzes variations in infrared energy to support reliable facial image acquisition under varying environmental and lighting conditions.

[0029] The combined outputs of the imaging unit 105, three-dimensional depth sensor, and infrared sensor are fused and processed to generate a unified facial representation. The fused data undergoes correction operations including contrast enhancement, geometric alignment, and distortion compensation to improve accuracy and consistency. The integrated processing enables precise facial image acquisition, ensuring reliable input data for subsequent analytical and recognition operations performed by the microcontroller.

[0030] Further, the imaging unit 105 enables an AI module to utilize FER (facial expression recognition) protocols like Facial Action Coding System (FACS) to match the examinee against the retrieved profile from the database by the microcontroller. The Facial Action Coding System (FACS) operates by analyzing facial muscle movements by breaking expressions into action units to interpret human emotions. The AI module performs facial expression recognition (FER) protocols by normalizing input facial data into a standardized representation, followed by detecting facial landmarks including eyes, nose and lips through trained regression to generate coordinate mappings.

[0031] A feature extraction stage computes geometric ratios and encodes expression-specific micro-movement patterns into high-dimensional vectors using convolutional layers. These features are encoded into a normalized embedding vector and compared with the stored reference profile retrieved from the database via the microcontroller using similarity metrics. The module further performs temporal analysis to evaluate expression consistency for liveness detection and spoof prevention. A combined confidence score based on facial matching and FER-based expression analysis is generated and compared against a predefined threshold to authenticate the examinee.

[0032] If the AI module detects a mismatch in the examinee identification, then the microcontroller triggers an alert by an embedded speaker 106. The embedded speaker 106 operates by receiving an electrical alert signal from the microcontroller, which is processed through a signal conditioning stage to generate an audio-frequency waveform corresponding to a predefined alert tone. The signal is amplified using an integrated power amplifier and supplied to a transduction element that converts the electrical waveform into mechanical vibrations. The vibrations are propagated through a diaphragm structure to produce audible sound waves in the surrounding medium. The microcontroller utilizes the stored digital audio patterns to ensure controlled amplitude, consistent sound output, and rapid activation upon mismatch detection.

[0033] An acoustic echo biometric verification module is installed at the examination entrance, which is activated upon confirmed physical presence of the examinee. The acoustic echo biometric verification module comprises an ultrasonic transducer that emits inaudible ultrasonic pulses towards the examinee’s ear and an ultrasonic receiver that captures reflected signals. The ultrasonic transducer operates by converting an electrical excitation signal into mechanical vibrations through a piezoelectric element that expands and contracts in response to alternating voltage, thereby generating controlled ultrasonic pressure waves at a defined frequency directed as a beam towards the ear structure.

[0034] Upon interaction with ear canal geometry and surrounding surfaces, the waves undergo reflection, scattering, and attenuation. The ultrasonic receiver operates by capturing the returning pressure waves and converting them into electrical signals via a reverse piezoelectric effect, followed by amplification, filtering, and time-delay analysis to extract amplitude and phase variations for generating echo profiles representing structural characteristics.

[0035] The captured data is then processed by the AI module using convolutional neural network architectures, wherein the input is first normalized and structured into multi-dimensional feature maps representing spatial and frequency-domain characteristics. The CNN comprises stacked convolutional layers that apply learnable filters to extract hierarchical features corresponding to ear canal geometry, cartilage structure, and cavity dimensions. Each convolution operation is followed by nonlinear activation functions and pooling layers to reduce dimensionality while preserving salient structural patterns. The extracted feature maps are progressively transformed through deeper layers into high-level embeddings, which are flattened and passed to fully connected layers for compact representation. The final output layer generates a unique Ear Resonance Profile used for final identity confirmation through pattern matching.

[0036] An interactive display panel 107 is installed at the examination center entrance to present the authenticated examinee’s assigned room number, seat number, and precise seating location. The interactive display panel 107 operates by receiving processed authentication and allocation data from the microcontroller. A display driver circuit converts the received digital information into graphical and textual output signals, and a rendering operation organizes the data into structured visual formats indicating room number, seat number, and precise seating coordinates. The pixel matrix is updated through sequential scanning and refresh cycles to ensure real-time display consistency. The microcontroller regulates the brightness, contrast, and layout rendering for optimal visibility, enabling dynamic updates upon authentication events for accurate and synchronized navigation guidance to the examinee.

[0037] A storage compartment 108 is provided at the examination entrance to store rough sheets and writing tools for the examinee. The storage compartment 108 comprises of, but not limited to, internally partitioned slots designed to segregate and maintain items in predefined positions for systematic retrieval.

[0038] In an embodiment of the present invention, the storage compartment 108 used herein comprises of a locking unit that is actuated through an electromechanical actuator to open and close the storage compartment 108 in a controlled manner. The locking unit operates by converting an electrical control signal into mechanical motion through a drive operation comprising a coil-driven solenoid gear assembly. Upon activation the actuator displaces a locking pin between engaged and disengaged positions, and securing or releasing the storage compartment 108 door. The locking unit further includes a guided slider and return spring operation to ensure precise alignment and automatic re-locking after actuation, providing controlled access, mechanical stability, and secure retention of stored examination materials during operation.

[0039] A robotic gripper arm 109 is mounted on a slider 110 mounted at the examination entrance that retrieves and presents the rough sheets and writing tools to the individual examinee upon successful identity confirmation. The robotic gripper arm 109 operates as an electromechanical manipulator driven by control signals from the microcontroller to coordinate motion through motorized joints. Rotary actuators generate controlled torque at articulated joints enabling multi-axis positioning, while a gear transmission operation ensures torque amplification and positional accuracy. The end-effector gripper converts rotational motion into linear gripping force through an actuator-driven linkage for controlled grasping and release of examination materials, and the microcontroller synchronizes joint movements for smooth trajectory execution during retrieval and placement operations.

[0040] The slider 110 operates as a linear motion assembly mounted within a guide rail to facilitate controlled translational movement of the robotic gripper arm 109, wherein a motor-driven drive unit converts electrical input into rotational motion transmitted through a lead screw operation to achieve precise linear displacement. A carriage unit moves along low-friction guide rails supported by bearings to maintain alignment and reduce resistance. The drive operation executes calibrated motion control to ensure accurate forward and reverse positioning, thereby providing stable and repeatable linear actuation for precise spatial deployment of the robotic gripper during rough sheets, writing tools retrieval and delivery operations, thus, minimizing human intervention and maintaining the examination process integrity.

[0041] A plurality of color-coded LED (light emitting diode) strips 111 are embedded throughout the examination center flooring, to illuminate a unique color-coded navigation path corresponding to each examinee’s assigned location. Each LED strips 111 operates through a semiconductor p-n junction that emits photons when forward biased. Upon application of electrical current, the electrons from the n-region recombine with holes in the p-region, releasing energy as light through electroluminescence. The emitted wavelength, and thus color, is determined by the bandgap of the semiconductor materials used. In RGB LED strips 111 configurations, individual red, green, and blue semiconductor elements are selectively driven with controlled current levels. A driver circuit regulates voltage and current to each LED channel, while pulse-width modulation controls duty cycle to achieve precise brightness and color blending for formation of distinct navigation paths.

[0042] The LED strips 111 routes operate in conjunction with a network of surveillance cameras 104 that are installed throughout the examination center to monitor examinee movement. Each surveillance camera 104 operates by capturing visual scenes as sequential frames and converting incident light into electrical signals through an image sensing operation. The acquired analog signals are digitized by an onboard processing unit to generate structured pixel data forming video frames. An image processing is carried out to reduce noise and correct colour variations so as to improve the clarity and accuracy of the captured feed. The processed frames are then encoded using compression operations to enable efficient transmission and storage and are continuously streamed to the microcontroller. The microcontroller processes the incoming video feed in real time to identify movement patterns of examinees across the LED-guided routes for continuous monitoring. Upon confirmation that the examinee has reached the designated location, the microcontroller then dynamically reassigns the LED strips 111 to show the respective route for subsequent examinees.

[0043] Each examination desk 203 for the examinees is equipped with a plurality of thermal sensors 201 and microphones 202. The thermal sensors 201 operate by detecting infrared radiation emitted from objects and converting the received thermal energy into electrical signals through a thermoelectric conversion operation. The generated analog signals are conditioned through amplification and filtering stages to reduce noise and stabilize signal levels. The processed thermal data is digitized by an onboard processing assembly to form a spatial temperature matrix representing heat distribution across the desk 203 surface, thereby enabling generation of movement heat maps, and is transmitted to the microcontroller for further analysis.

[0044] The microphones 202 operate by converting the acoustic pressure variations in the surrounding environment into corresponding electrical signals through a transduction operation. Incident sound waves cause a diaphragm to vibrate in accordance with pressure fluctuations for producing proportional electrical variations. The weak analog signals are passed through a pre-amplification stage to enhance signal strength while maintaining low noise characteristics, followed by a filtering stage to remove unwanted frequency components and improve speech clarity. The conditioned signals are then digitized by an onboard processing unit to form discrete audio data streams organized into time-sequenced frames for further interpretation and capture of examinee conversations.

[0045] The movement-based heat maps from the thermal sensors 201 and the audio signals from the microphones 202 are analyzed by the AI module using trained machine learning protocols like Support Vector Machine (SVM) classification protocol to detect suspicious movement patterns or conversations. The Support Vector Machine (SVM) classification protocol classifies the input data by identifying an optimal hyperplane that separates normal and suspicious behavioral patterns based on learned feature vectors. The AI module operates by receiving the processed input data streams and converting them into structured feature representations suitable for analysis.

[0046] The received data is normalized and transformed into numerical feature vectors, which are evaluated using trained models by the trained machine learning protocols and developed through supervised learning on labelled datasets. The trained models perform pattern recognition by applying weighted computations across multiple layers to extract spatial, temporal, and behavioural correlations from the thermal and audio inputs. The extracted features are compared against learned reference patterns to identify deviations and classify observed activities, and a scoring operation generates confidence values indicating suspicion levels for further decision-making.

[0047] A plurality of cascading plates 204 is mounted on guide rails 205 that are installed on each lateral side of the desk 203 to be deployed for forming a physical barrier around the examinee and restricting opportunities for malpractice. The cascading plates 204 operate as sequentially deployable barrier elements arranged in an overlapping configuration along a mounting assembly. Each cascading plate 204 is coupled to an actuation linkage that receives control signals from the microcontroller to enable controlled extension and retraction. Upon activation, the cascading plates 204 are driven through a sequential actuation sequence in which a primary driving element initiates movement of the leading plate 204, followed by transfer of motion to successive plates 204 through mechanically linked coupling, thereby forming a progressive enclosure around the examinee. The overlapping geometry ensures continuous coverage, while interlocking edge portions enhance structural rigidity and alignment during deployment. Retraction is executed in reverse order through coordinated disengagement of the actuation linkages, enabling the plates 204 to return into a compact stacked configuration for reset and reuse.

[0048] The guide rails 205 function as linear structural tracks that are installed on each lateral side of the desk 203 to define a constrained path for movement of the cascading plates 204. The guide rails 205 are formed as elongated channels with precision-machined low-friction surfaces that minimize resistance during translational motion while maintaining strict alignment. The cascading plates 204 engage with the guide rail 205 through guided coupling elements that restrict movement to a single linear axis, thereby eliminating lateral or rotational deviation during operation. Internal guide grooves within the guide rails 205 maintain continuous alignment of successive plates 204 and ensure smooth sliding engagement throughout deployment and retraction cycles. Mechanical end-stop assemblies are integrated at both terminal positions to limit travel, absorb impact at full extension and retraction, and ensure repeatable positioning accuracy.

[0049] A linked user interface is provided to enable the authorized personnel to input examination center parameters, including total room count, seating capacities, and examinee volume. Based on the inputs, the AI module generates an optimized seating plan by using trained machine learning protocols for ensuring efficient allocation and spacing of examinees. The linked user interface captures the structured input data and transmits the data to the microcontroller through a communication module. The received data is normalized and converted into structured numerical representations and provided to the AI module.

[0050] The communication module mentioned herein includes, but not limited to Wi-Fi (Wireless Fidelity) module, Bluetooth module, GSM (Global System for Mobile Communication) module. The communication module used in the system is preferably the Wi-Fi module. The Wi-Fi module enables wireless communication by transmitting and receiving data over radio frequencies using IEEE 802.11 protocols. The Wi-Fi module connects to a network via an access point, converting digital data into radio signals. The Wi-Fi module processes TCP/IP protocols for data exchange, interfaces with microcontrollers through UART/SPI, and ensures encrypted communication using WPA/WPA2 security standards for secure and efficient wireless connectivity.

[0051] The AI module processes the inputs using trained machine learning protocols, wherein the trained machine learning protocols operate through pre-trained computational models developed from historical labeled datasets. The input parameters are transformed into numerical feature vectors and processed through interconnected computational layers applying learned weighted coefficients to identify spatial patterns, constraints, and correlations. Forward computation is performed to evaluate allocation possibilities, followed by iterative refinement based on capacity utilization and spacing requirements. A ranked seating plan is generated, and the highest-scoring output is selected for optimized allocation of examinees across rooms and seats.

[0052] The AI module is further processes the imaging data received from the surveillance cameras 104 to identify suspicious behaviors, including abnormal head movements, unauthorized person entry into frames, gaze direction anomalies, and lip movements. Based on the observations, the microcontroller generates a suspicion score for each examinee, and when the score exceeds a predefined threshold, the microcontroller triggers an alert via the speaker 106 to notify authorities.

[0053] The AI module operates on the received imaging data by first converting video frames into structured numerical representations, wherein spatial features are extracted to represent facial orientation, body posture, and motion trajectories over time. The data is normalized and processed through trained computational layers that evaluate temporal and spatial variations across consecutive frames. Pattern recognition operations identify deviations from expected behavioural baselines by analysing head movement vectors, gaze direction alignment, and lip motion consistency, while also detecting frame-level anomalies indicative of unauthorized presence. The extracted features are continuously aggregated to form a behavioural profile for each examinee. The suspicion score evaluates the likelihood of suspicious activity by comparing observed patterns against learned reference behaviours, and the resulting value is transmitted to the microcontroller for alert decision execution by the speaker 106.

[0054] A battery (not shown in figure) is associated with the device to supply power to electrically powered components which are employed herein. The battery is comprised of a pair of electrode named as a cathode and an anode. The battery uses a chemical reaction of oxidation/reduction to do work on charge and produce a voltage between their anode and cathode and thus produces electrical energy.

[0055] The present invention works best in the following manner, wherein an examinee first arrives at the examination center entrance where a multi-factor biometric authentication assembly performs sequential identity verification using fingerprint matching, facial recognition with expression analysis, and acoustic echo biometric verification module. Upon successful authentication, the microcontroller retrieves the examinee’s profile from a linked database and activates an interactive display panel 107 that displays the assigned room number, seat number, and precise seating location. The examinee is then guided through a color-coded LED strips 111 embedded in the flooring and continuously monitored by surveillance cameras 104 for movement tracking. Upon reaching the designated desk 203, examination materials are dispensed from a secured storage compartment 108 using a robotic gripper arm 109 mounted on a slider 110. During the examination, thermal sensors 201 and microphones 202 embedded in the desk 203 generate movement heat maps and capture audio data, which are analysed by the AI module using trained machine learning protocols along with surveillance imaging data to detect suspicious behaviour. Cascading plates 204 mounted on guide rails 205 are deployed to form a physical barrier around the examinee, while the microcontroller continuously evaluates behavioural patterns and triggers alerts via an embedded speaker 106 when predefined thresholds are exceeded.

[0056] Although the field of the invention has been described herein with limited reference to specific embodiments, this description is not meant to be construed in a limiting sense. Various modifications of the disclosed embodiments, as well as alternate embodiments of the invention, will become apparent to persons skilled in the art upon reference to the description of the invention. , Claims:1) A multi-factor authentication and cheating prevention system, comprising:

i) a multi-factor biometric authentication assembly installed at an examination center entrance configured to perform individual examinee verification and generate an alert via an embedded speaker 106 upon mismatch detection;
ii) an acoustic echo biometric verification module installed at the entrance of the examination center and configured to be activated upon confirmed physical presence and capture ear canal geometry, cartilage structure and cavity dimensions, enabling an AI (artificial intelligence) module integrated with a connected microcontroller to generate an individual Ear Resonance Profile for final identity confirmation;
iii) an interactive display panel 107 installed at the examination center entrance and configured to present the authenticated examinee's assigned room number, seat number and precise seat location;
iv) a plurality of thermal sensors 201 and microphones 202 embedded in each auxiliary examination desk 203, configured to generate movement heat maps, and capture examinee conversations, enabling the connected AI module to utilize trained machine learning protocols for identifying suspicious movement and behavior and accordingly trigger an alert notification; and
v) a plurality of cascading plates 204 mounted on a guide rail 205 installed on each lateral side of the examination desk 203 and configured to deploy along the guide rails 205 forming a barrier around the examinee to prevent malpractice attempts.

2) The multi-factor authentication and cheating prevention system as claimed in claim 1, wherein the multi-factor biometric authentication assembly comprises:
i) a foot platform 101 incorporating a plurality of pressure sensors 102 for detecting user presence, enabling the microcontroller to activate the sequential authentication operations;
ii) a fingerprint scanner 103 to scan the examinee’s fingerprint for retrieving individual pre-fed profiles to a linked database;
iii) an imaging unit 105 with an integrated three-dimensional depth sensor and an infrared sensor configured to capturing facial images, enabling the AI module to utilize FER (facial expression recognition) protocols to match the examinee against the retrieved profile, enabling the microcontroller to trigger an alert upon mismatch detection.

3) The multi-factor authentication and cheating prevention system as claimed in claim 1, wherein the acoustic echo biometric verification module comprises an ultrasonic transducer for emitting inaudible ultrasonic pulses directed towards the examinee’s ear and an ultrasonic receiver for capturing the reflected waves, enabling the AI module to analyze individual examinee’s ear structure using CNN (convolutional neural network) architectures for final identity confirmation.

4) The multi-factor authentication and cheating prevention system as claimed in claim 1, wherein a storage compartment 108 is installed at the examination center entrance and configured to store rough sheets and writing tools for the examinees, enabling a robotic gripper arm 109 mounted on a slider 110 installed at the entrance to retrieve and present the rough sheets and writing tools to individual examinees upon identity confirmation.

5) The multi-factor authentication and cheating prevention system as claimed in claim 1, wherein a plurality of color-coded LED (light emitting diode) strips 111 embedded throughout the examination center flooring and configured to illuminate a unique color-coded route corresponding exclusively to each individual examinee’s assigned navigation path with a plurality of linked surveillance cameras 104 positioned throughout the center for monitoring examinee movement, enabling the microcontroller to reassign the color-coded route for subsequent examinee upon confirmed arrival of the examinee at the designated spot.

6) The multi-factor authentication and cheating prevention system as claimed in claim 1, wherein a linked user interface is configured to enable authorized personnel to input examination center data including total room count, individual room capacities and total examinee numbers, enabling the connected AI module to generate an optimized seating plan by utilizing trained machine learning protocols.

7) The multi-factor authentication and cheating prevention system as claimed in claim 1, wherein the AI module is further configured to receive the imaging data from the surveillance cameras 104 for analysis and identification of suspicious head movement patterns, unauthorized person frame entry, individual examinee gaze direction and lip movements, generating a suspicion score for each examinee and enabling the microcontroller to trigger an alert via the speaker 106 upon detection of the suspicion score exceeding a threshold value.

8) The multi-factor authentication and cheating prevention system as claimed in claim 1, wherein a communication module is integrated with the microcontroller, configured to establish wireless connectivity with a computing unit, inbuilt with the user interface, enabling remote monitoring and control of operations.