DESC:TECHNICAL FIELD
[0001] The present disclosure is generally related to artificial intelligence, and more particularly relates to the method and system for determining the authenticity of a user using an artificial intelligence model.
BACKGROUND
[0002] In an era where trust plays a pivotal role in various aspects of life, ranging from employment to business partnerships, tourism, and tenancy, the need for robust trust assessment systems has become increasingly vital. Traditional methods of identity authentication often rely on verification through government-issued identification documents and testimonies, utilizing a limited set of parameters. These methods, however, present challenges, including the dependence on fixed identification (ID) documents and the verification of details against government databases with inherent gaps.
[0003] The existing trust assessment techniques primarily hinge on the verification of identification documents issued by the government and testimonies. These techniques, although widely employed, come with inherent limitations. One of the key drawbacks is the reliance on a fixed set of identification documents, which may not be comprehensive enough to capture the diverse aspects of an individual's identity. In scenarios such as employment, business partnerships, tourism, or tenancy, where trust is a critical factor, a more nuanced and advanced approach is required.
[0004] The authentication of identity is often constrained by the available data and parameters. The verification process is typically confined to cross-referencing details from government databases, a method that may result in information gaps. For instance, individuals may possess multiple identities with variations in biometric data, making it challenging to establish a foolproof authentication process. Moreover, the data used for identity verification is frequently unstructured at the backend, leading to manual interventions and errors in the authentication process.
[0005] To overcome these challenges and enhance the reliability of trust assessment systems, a paradigm shift is needed. The future of identity authentication lies in the adoption of advanced technologies and methodologies that go beyond traditional means. This necessitates the development of a trust assessment system that leverages cutting-edge techniques, including Artificial Intelligence (AI) and machine learning algorithms.
[0006] In conclusion, the limitations of traditional trust assessment techniques underscore the need for a paradigm shift towards more advanced and adaptable systems. The integration of AI and machine learning in trust assessment can revolutionize the authentication process, making it more robust, accurate, and efficient. As we embrace the potential of technology, the future of trust assessment systems holds the promise of providing a secure foundation for critical decisions in multiple areas, but not limiting to, such as employment, business partnerships, tourism, and tenancy, and any other areas wherein authenticity check may be a prerequisite.
SUMMARY
[0007] This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention. This summary is neither intended to identify essential inventive concepts of the invention nor is it intended for determining the scope of the invention.
[0008] According to one embodiment of the present disclosure, a method for determining the authenticity of a user based on an artificial intelligence (AI) model is disclosed. The method includes determining an image score based on matching a captured image with a document image, wherein the captured image has the user in at least one image frame. Further, the method includes determining a short video score based on matching a captured short video with the document image, wherein the captured short video has the user in at least one short video frame. Further, the method includes determining a liveliness factor associated with the user based on a dialogue narrated by the user and the captured short video. Furthermore, the method includes determining a location score based on matching a captured geographical coordinates corresponding to the user with an input location. Furthermore, the method includes computing a total authentic score based on the image score, the short-video score, and the location score such that the authenticity of the user is determined if the total authentic score exceeds a pre-defined threshold.
[0009] To further clarify the advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which are illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail in the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
[0011] FIGURE 1 is a block diagram depicting an exemplary system for determining an authenticity of a user using an artificial intelligence (AI) model, in accordance with an embodiment of the present disclosure;
[0012] FIGURE 2 illustrates a process flow diagram depicting an exemplary method for determining the authenticity of the user using the AI model, in accordance with an embodiment of the present disclosure;
[0013] FIGURE 3 illustrates a sub-process flow diagram depicting an exemplary sub-method for determining an image score, according to an embodiment of the present disclosure;
[0014] FIGURE 4 illustrates a sub-process flow diagram depicting an exemplary sub-method for determining a short-video score, according to an embodiment of the present disclosure;
[0015] FIGURE 5 illustrates a sub-process flow diagram depicting an exemplary sub-method for determining a liveliness factor, according to an embodiment of the present disclosure;
[0016] FIGURE 6a-6b illustrates an exemplary use case for matching a captured image with a document image, according to an embodiment of the present disclosure;
[0017] FIGURE 7a-7b illustrates another exemplary use case for matching the captured image with the document image, according to an embodiment of the present disclosure; and
[0018] FIGURE 8 illustrates another exemplary use case for matching the captured image with the document image, according to an embodiment of the present disclosure.
[0019] Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present invention. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.
DETAILED DESCRIPTION
[0020] For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the various embodiments and specific language will be used to describe the same. It should be understood at the outset that although illustrative implementations of the embodiments of the present disclosure are illustrated below, the present invention may be implemented using any number of techniques, whether currently known or in existence. The present disclosure is not necessarily limited to the illustrative implementations, drawings, and techniques illustrated below, including the exemplary design and implementation illustrated and described herein, but may be modified within the scope of the present disclosure.
[0021] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are explanatory of the invention and are not intended to be restrictive thereof.
[0022] Reference throughout this specification to “an aspect”, “another aspect” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrase “in an embodiment”, “in another embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.
[0023] It is to be understood that as used herein, terms such as, “includes,” “comprises,” “has,” etc. are intended to mean that the one or more features or elements listed are within the element being defined, but the element is not necessarily limited to the listed features and elements, and that additional features and elements may be within the meaning of the element being defined. In contrast, terms such as, “consisting of” are intended to exclude features and elements that have not been listed.
[0024] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted to not unnecessarily obscure the embodiments herein. Also, the various embodiments described herein are not necessarily mutually exclusive, as some embodiments can be combined with one or more other embodiments to form new embodiments. The term “or” as used herein, refers to a non-exclusive or unless otherwise indicated. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein can be practiced and to further enable those skilled in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0025] As is traditional in the field, embodiments may be described and illustrated in terms of blocks that carry out a described function or functions. These blocks, which may be referred to herein as units or modules or the like, are physically implemented by analog or digital circuits such as logic gates, integrated circuits, microprocessors, microcontrollers, memory circuits, passive electronic components, active electronic components, optical components, hardwired circuits, or the like, and may optionally be driven by firmware and software. The circuits may, for example, be embodied in one or more semiconductor chips, or on substrate supports such as printed circuit boards and the like. The circuits constituting a block may be implemented by dedicated hardware, by a processor (e.g., one or more programmed microprocessors and associated circuitry), or by a combination of dedicated hardware to perform some functions of the block and a processor to perform other functions of the block. Each block of the embodiments may be physically separated into two or more interacting and discrete blocks without departing from the scope of the invention. Likewise, the blocks of the embodiments may be physically combined into more complex blocks without departing from the scope of the invention.
[0026] The accompanying drawings are used to help easily understand various technical features and it should be understood that the embodiments presented herein are not limited by the accompanying drawings. As such, the present disclosure should be construed to extend to any alterations, equivalents, and substitutes in addition to those which are particularly set out in the accompanying drawings. Although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are generally only used to distinguish one element from another.
[0027] FIGURE 1 is a block diagram depicting an exemplary system 101 for determining authenticity of a user 104 using an artificial intelligence (AI) model, in accordance with an embodiment of the present disclosure. In an embodiment, the system 101 may be implemented in a remote server or referred to as an authenticity check platform 103 and in communication with the user 104.
[0028] The system includes a processor 107, a memory 109, a database 111, an artificial intelligence (AI) model 113, module(s) 115, the platform 105, and components thereof, coupled with each other.
[0029] In an example, the processor(s) 107 may be a single processing unit or a number of units, all of which could include multiple computing units. The processor 107 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logical processors, virtual processors, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor 107 is configured to fetch and execute computer-readable instructions and data stored in memory 109 to perform one or more methods, as discussed herein throughout the present disclosure.
[0030] The memory 109 may include any non-transitory computer-readable medium known in the art including, for example, volatile memory or Random Access Memory (RAM), such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read-only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.
[0031] At least one of a plurality of operations of the system 101 may be implemented through an AI model 113. A function associated with AI may be performed through the non-volatile memory, the volatile memory, and the processor 107.
[0032] The processor 107 may alternatively be referred to as one or a plurality of processors, within the scope of the present disclosure. At this time, one or a plurality of processors 107 may be a general purpose processor, such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and/or an AI-dedicated processor such as a neural processing unit (NPU).
[0033] The one or a plurality of processors 107 control the processing of the input data in accordance with a predefined operating rule or artificial intelligence (AI) model 113 stored in the non-volatile memory and the volatile memory. The predefined operating rule or AI model 113 is provided through training or learning.
[0034] Here, being provided through learning means that, by applying a learning technique to a plurality of learning data, a predefined operating rule or AI model 113 of a desired characteristic is made. The learning may be performed in a device itself in which AI according to an embodiment is performed, and/or may be implemented through a separate server/system.
[0035] The AI model 113 may consist of a plurality of neural network layers. Each layer has a plurality of weight values and performs a layer operation through the calculation of a previous layer and an operation of a plurality of weights. Examples of neural networks include but are not limited to, convolutional neural network (CNN), deep neural network (DNN), recurrent neural network (RNN), restricted Boltzmann Machine (RBM), deep belief network (DBN), bidirectional recurrent deep neural network (BRDNN), generative adversarial networks (GAN), and deep Q-networks.
[0036] The learning technique is a method for training the AI model 113 initially with a European population dataset. Further, the AI model 113 may be re-trained using new Indian facial data along with the initial dataset which leads to parameter optimization leading to better accuracy on Indian demographics. Consequently, the retrained AI model 113 may learn to identify new facial key points for measurements which differentiates Indian faces better as compared to faces. Examples of learning techniques include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.
[0037] According to the disclosure, the processor may perform a pre-processing operation on the data to convert it into a form appropriate for use as input for the AI model 113. The artificial intelligence model may be obtained by training. Here, “obtained by training” means that a predefined operation rule or artificial intelligence model configured to perform a desired feature (or purpose) is obtained by training a basic artificial intelligence model with multiple pieces of training data by a training technique. The artificial intelligence model may include a plurality of neural network layers. Each of the plurality of neural network layers includes a plurality of weight values and performs neural network computation by computation between a result of computation by a previous layer and the plurality of weight values.
[0038] The database 111 may include one or more database repositories for storing data, such as images used for training the AI model 113.
[0039] As an example, the module(s) 115 may include a program, a subroutine, a portion of a program, a software component, or a hardware component capable of performing a stated task or function. As used herein, the module(s) 115 may be implemented on a hardware component such as a server independently of other modules, or a module can exist with other modules on the same server, or within the same program. The module(s) 115 may be implemented on a hardware component, such as processor one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. The module(s) 115 when executed by the processor(s) 107 may be configured to perform any of the functionalities discussed herein.
[0040] In an embodiment, the module(s) 115 may be implemented using one or more artificial intelligence (AI) modules that may include a plurality of neural network layers. Examples of neural networks include but are not limited to, Convolutional Neural Network (CNN), Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Restricted Boltzmann Machine (RBM). Further, ‘learning’ may be referred to in the disclosure as a method for training the AI model 113 using a plurality of learning data to match a captured image or a captured short video with a document image such that the authenticity of the user 104 may be established. Examples of learning techniques include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. At least one of a plurality of CNN, DNN, RNN, RMB models and the like may be implemented to thereby achieve execution of the present subject matter’s mechanism through an AI model. A function associated with an AI module may be performed through the non-volatile memory, the volatile memory, and the processor. The processor may include one or a plurality of processors.
[0041] At this time, one or a plurality of processors may be a general-purpose processor, such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and/or an AI-dedicated processor, such as a neural processing unit (NPU). One or a plurality of processors control the processing of the input data in accordance with a predefined operating rule or artificial intelligence (AI) model stored in the non-volatile memory and the volatile memory. The predefined operating rule or artificial intelligence model is provided through training or learning.
[0042] The modules 115 may include a set of instructions that may be executed to cause the authenticity check platform 103 to perform authenticity of the user 104.
[0043] In an example, the user 104 may interact with the authenticity check platform 103. In the example, the interaction may be done using a user device (not shown) accessing an online version of the authenticity check platform 103 i.e., access via the remote server. Alternatively, the authenticity check platform 103 may be installed locally as an application in the user’s device (not shown). Thus, the user 104 is in communication with the authenticity check platform 103.
[0044] Further, in the example, the authenticity check platform 103 may generate a prompt to the user 104 for producing a document. In the example, the document may correspond to any government verification document such as driving license, passport, etc. Accordingly, the document may include a face image and details of the user 104. Further, the authenticity check platform 103 may access a camera of the user device (not shown) to generate the document image, alternatively, the user 104 may upload the document image directly with the authenticity check platform 103. Consequently, the authenticity check platform 103 may receive the document image containing the face image and details of the user 104.
[0045] Further, in the example, the authenticity check platform 103 may be configured to access the camera to capture the image or the short video of the user 104. Consequently, the authenticity check platform 103 may have the document image and the captured image or the short video of the user 104.
[0046] Furthermore, in the example, the authenticity check platform 103 may be configured to determine the authenticity of the user using the AI model 113, such that the authenticity check platform 103 provides, whether the face image in the document image matches the captured image or the short video of the user 104.
[0047] Consequently, the authenticity check platform 103 may compute a total authentic score based on the captured image, the short video, and a location such that the authenticity of the user 104 is determined if the total authentic score exceeds a pre-defined threshold. Additionally, the authenticity check platform 103 may be configured to determine a liveliness factor associated with the user 104. A detailed explanation of this process performed by the system 101 is explained in forthcoming paragraphs.
[0048] FIGURE 2 illustrates a process flow diagram depicting an exemplary method 200 for determining the authenticity of the user 104 using the AI model 113, in accordance with an embodiment of the present disclosure. The method 200 includes a series of operations 202 through 210 executed by one or more components of the system 101, in particular the processor 107, the AI model 113, and the modules 115.
[0049] At step 202, the method 200 may include determining an image score based on matching the captured image with the document image. In an example, the captured image has the user 104 in at least one image frame.
[0050] At step 204, the method 200 may include determining the short video score based on matching the captured short video with the document image. In an example, the captured short video has the user 104 in at least one short video frame.
[0051] At step 206, the method 200 may include determining the liveliness factor associated with the user 104 based on a dialogue narrated by the user and the captured short video.
[0052] At step 208, the method 200 may include determining a location score based on matching a captured geographical coordinates corresponding to the user 104 with an input location.
[0053] At step 210, the method 200 may include computing a total authentic score based on the image score, the short-video score, and the location score such that the authenticity of the user 104 is determined if the total authentic score exceeds a pre-defined threshold.
[0054] FIGURE 3 illustrates a sub-process flow diagram depicting an exemplary sub-method 202 for determining the image score, according to an embodiment of the present disclosure.
[0055] At step 302, the sub-method 202 may include generating a prompt by the authenticity check platform 103. The prompt may be displayed on a screen of the user’s device which provides access to the authenticity check platform 103 to the user 104. In an example, the prompt may indicate an instruction to the user 104 to capture the image using the camera of a user’s equipment or the user’s device.
[0056] At step 304, the sub-method 202 may include detecting at least one face within the captured image and the document image.
[0057] At step 306, the sub-method 202 may include determining a vertical orientation of the detected at least one face within the captured image.
[0058] At step 308, the sub-method 202 may include rotating the captured image at least 90° upon determining the vertical orientation is not aligned vertically.
[0059] At step 310, the sub-method 202 may include extracting a set of features corresponding to the detected at least one face in the captured image and the document image respectively. In an example, the set of features may be extracted using the AI model 113.
[0060] At step 312, the sub-method 202 may include comparing a set of image parameters associated with the extracted set of features corresponding to the captured image and the document image respectively using the AI model 113. In an example, the set of image parameters may indicate a location, the at least one frame, and an encoding.
[0061] Consequently, at step 314 the sub-method 202 may include determining the image score based on comparing the set of image parameters. Thus, the image score may correspond to the likelihood of matching the captured image with the document image. In an example, a higher image score may signify that the user’s 104 face in the captured image may match the face in the document image.
[0062] FIGURE 4 illustrates a sub-process flow diagram depicting an exemplary sub-method 204 for determining the short video score, according to an embodiment of the present disclosure.
[0063] At step 402, the sub-method 204 may include generating the prompt by the authenticity check platform 103. The prompt may be displayed on the screen of the user’s device. In an example, the prompt may indicate an instruction to the user 104 to capture the short video using the camera of the user device.
[0064] At step 404, the sub-method 204 may include detecting at least one face within the captured short video and the document image.
[0065] At step 406, the sub-method 204 may include determining a vertical orientation of the detected at least one face within the captured short video.
[0066] At step 408, the sub-method 204 may include rotating the captured short-video at least 90° upon determining the vertical orientation is not aligned vertically
[0067] At step 410, the sub-method 204 may include extracting the set of features corresponding to the detected at least one face in the captured short-video and the document image respectively. In an example, the set of features may be extracted via the AI model 113.
[0068] At step 412, the sub-method 204 may include comparing a set of video parameters associated with the extracted set of features corresponding to the captured short video and the document image respectively using the AI model 113. In an example, the set of image parameters indicates a location, the at least one frame, and the encoding.
[0069] Consequently, at step 414, the sub-method 204 may include determining the short video score based on comparing the set of video parameters. Thus, the short video score may correspond to the likelihood of matching the captured short video with the document image. In an example, a higher short video score may signify that the user’s 104 face in the captured short video score may match the face in the document image.
[0070] FIGURE 5 illustrates a sub-process flow diagram depicting an exemplary sub-method 206 for determining the liveliness factor, according to an embodiment of the present disclosure.
[0071] At step 502, the sub-method 206 may include generating the prompt indicative of instructing the user 104 to narrate a dialogue displayed on the display screen of the user device.
[0072] At step 504, the sub-method 206 may include recording an audio input based on the dialogue narrated by the user 104.
[0073] At step 506, the sub-method 206 may include extracting a set of features corresponding to at least one of the audio inputs and user movement data via the captured short video. In an example, the set of features may be extracted using the AI model 113.
[0074] At step 508, the sub-method 206 may include comparing a set of audio parameters associated with the extracted set of features and the dialogue using the AI model 113.
[0075] Consequently, at step 510, the sub-method 206 may include determining the liveliness factor based on the compared set of audio parameters and the user movement data. In an example, the liveliness factor represents a measure or assessment of how lively or active the user 104 is and may be derived through a specific analytical process. In the example, the determination of the liveliness factor relies on the comparison of two sets of data i.e., the audio parameters which include various characteristics of audio data, which could involve the analysis of voice patterns, tone, pitch, or other audio features. The other set of data is the user’s 104 movement data which includes data related to the movement of the user 104. It could include information from sensors or devices that track physical movements, gestures, or actions.
[0076] FIGURE 6a-6b illustrates an exemplary use case for matching a captured image with a document image, according to an embodiment of the present disclosure.
[0077] Referring to Figure 6a, the document image 602 may contain a representation of a face (602a, presented as a printed image). In the context of the present invention, the authenticity check platform 103 performs a comparison between the captured image 604 and the depicted face 602a. In the illustration, the user 104 associated with the captured image 604 exhibits distinctive facial features like beards and moustaches, which are absent in the face 602a depicted in the document image 602. Despite these differences, the authenticity check platform 103 is configured to successfully align and match the face 602a with the captured image 604.
[0078] Similarly, referring to Figure 6b, the document image 602 may contain a representation of the face (602b, presented as a printed image). In the context of the present invention, the authenticity check platform 103 performs a comparison between the captured image 604 and the depicted face 602b. In the illustration, the user 104 associated with the captured image 604 exhibits distinctive facial features like eyeglasses, which are absent in the face 602b depicted in the document image 602. Despite this difference, the authenticity check platform 103 is configured to successfully align and match the face 602b with the captured image 604.
[0079] FIGURE 7a-7b illustrates another exemplary use case for matching the captured image with the document image, according to an embodiment of the present disclosure.
[0080] Referring to Figure 7a, the document image 702 may contain a representation of the face (702a, presented as the printed image). In the context of the present invention, the authenticity check platform 103 performs a comparison between the captured image 704 and the depicted face 702a. In the illustration, the user 104 associated with the captured image 704 exhibits distinctive features like the captured image is in colours, which is absent in the document image 602. Despite these differences, the authenticity check platform 103 is configured to successfully align and match the face 702a with the captured image 704.
[0081] Similarly referring to Figure 7b, the document image 702 may contain a representation of the face (702b, presented as the printed image). In the context of the present invention, the authenticity check platform 103 performs a comparison between the captured image 704 and the depicted face 702b. In the illustration, the user 104 associated with the captured image 704 exhibits distinctive features like the captured image may have saturated colours, which are absent in the document image 602. Despite these differences, the authenticity check platform 103 is configured to successfully align and match the face 702b with the captured image 704.
[0082] FIGURE 8 illustrates another exemplary use case for matching the captured image with the document image, according to an embodiment of the present disclosure.
[0083] Referring to Figure 8, the document image 802 may contain a representation of the face (802a, presented as the printed image). In the context of the present invention, the authenticity check platform 103 performs a comparison between the captured image 804 which has multiple faces and the depicted face 802a. In the illustration, the user 104 associated with the captured image 704 exhibits distinctive facial features like the change in facial structure due to age, eyeglasses and multiple faces in the captured image 804. Despite the differences, the authenticity check platform 103 is configured to successfully align and match the face 802a with the captured image 804.
[0084] While specific language has been used to describe the present subject matter, any limitations arising on account thereto, are not intended. As would be apparent to a person in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein. The drawings and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment.
,CLAIMS:1. A method for determining authenticity of a user based on an artificial intelligence (AI) model, the method comprising:
determining an image score based on matching a captured image with a document image, wherein the captured image has the user in at least one image frame;
determining a short-video score based on matching a captured short-video with the document image, wherein the captured short-video has the user in at least one short-video frame;
determining a liveliness factor associated with the user based on a dialogue narrated by the user and the captured short video;
determining a location score based on matching a captured geographical coordinates corresponding to the user with an input location; and
computing a total authentic score based on the image score, the short video score, and the location score such that authenticity of the user is determined if the total authentic score exceeds a pre-defined threshold.

2. The method as claimed in claim 1, wherein determining the image score comprises:
generating a prompt instructing the user to capture the image using a camera of a user equipment;
detecting at least one face within the captured image and the document image;
extracting a set of features corresponding to the detected at least one face in the captured image and the document image respectively;
comparing a set of image parameters associated with the extracted set of features corresponding to the captured image and the document image respectively using the AI model, wherein the set of image parameters indicates a location, the at least one frame, and an encoding; and
determining the image score based on comparing the set of image parameters.

3. The method as claimed in claim 2, wherein prior to extracting the set of features, the method comprises:
determining a vertical orientation of the detected at least one face within the captured image; and
rotating the captured image at least 90° upon determining the vertical orientation is not aligned vertically.

4. The method as claimed in claim 1, wherein determining the short-video score comprises:
generating a prompt instructing the user to capture the short video using a camera of a user equipment (UE);
detecting at least one face within the captured short video and the document image;
extracting a set of features corresponding to the detected at least one face in the captured short-video and the document image respectively;
comparing a set of video parameters associated with the extracted set of features corresponding to the captured short video and the document image respectively using the AI model, wherein the set of image parameters indicates a location, the at least one frame, and an encoding; and
determining the short video score based on comparing the set of video parameters.

5. The method as claimed in claim 4, wherein prior to extracting the set of features, the method comprises:
determining a vertical orientation of the detected at least one face within the captured short video; and
rotating the captured short-video at least 90° upon determining the vertical orientation is not aligned vertically.

6. The method as claimed in claim 1, wherein determining the liveliness factor comprises:
generating a prompt indicative of instructing the user to narrate the dialogue displayed on a display screen of a user equipment (UE);
recording an audio input based on the dialogue narrated by the user;
extracting a set of features corresponding to at least one of the audio input and a user movement data via the captured short video;
comparing a set of audio parameters associated with the extracted set of features and the dialogue using the AI model; and
determining the liveliness factor based on the compared set of audio parameters and the user movement data.

7. The method as claimed in claim in any of the preceding claims, comprises:
training the AI model using an extensive dataset comprising of a plurality of faces belonging to a specific geographic location.

8. A system for determining authenticity of a user based on an artificial intelligence model, the system comprising:
a memory;
at least one processor in communication with the memory, the at least one processor is configured to:
determine an image score based on matching a captured image with a document image, wherein the captured image has the user in at least one image frame;
determine a short-video score based on matching a captured short-video with the document image, wherein the captured short-video has the user in at least one short-video frame;
determine a liveliness factor associated with the user based on a dialogue narrated by the user;
determine a location score based on matching a captured geographical coordinates corresponding to the user with an input location; and
compute a total authentic score based on the image score, the short-video score, and the location score such that authenticity of the user is determined if the total authentic score exceeds a pre-defined threshold.