Description:Field of Invention
The present invention pertains to the field of surveillance and security purposes in military communication particularly focusing to create an automatic method for identifying any hostile or adversary presence. A mini seeker robot with face recognition addresses these issues by combining the speed of automated search with the accuracy of facial recognition technology. This innovation is vital in industries such as security, retail, and healthcare, where quick and precise identification is paramount for enhancing efficiency and ensuring safety and privacy compliance.
The objectives of this invention

The main objective is to develop the Mini Seeker Robot with Face Recognition stands out for its compact design and advanced facial recognition capabilities. Its small size allows it to navigate tight spaces efficiently, making it ideal for various environments. With its ability to identify and track faces, it offers enhanced security and personalized interactions, revolutionizing search and surveillance tasks.

Background of the invention
Face recognition technique for identification verification. The following are the three recommended actions for the same: (i) To compile a database of images of reliable or authorised individuals. (ii) Making use of the database to train the facial recognition software. (iii) To supply test data so that its accuracy can be verified. The Raspberry Pi interfaced with the Pi Camera Module V2, which was implemented by [ H Lee, R Grosse, R Ranganath et al. Communications of the ACM, vol. 54, no. 10, pp. 95-103, 2011]. OpenCV is the Python library utilized in the suggested system. Cascade Classifier is a recommended classification technique for face detection. The Python Thony IDE was used as the platform to run the application. [Jyotirmaya Ijaradar and Jinjing Xu, Current Journal of Applied Science and Technology, Vol.41, no.5, pp.1-12,2022] proposed a technique that might emphasize on certain areas of the face. The two algorithms namely, Local Binary Pattern Histogram (LBPH) and Haar Cascade are used to achieve this. Barik S. et al. [4] employed the concept of face detection and recognition security measure, which has robust support for Python and is modified by this proposed system by combining image processing and machine learning into one. claim that face recognition software may be developed on small, light devices such as Raspberry Pis, and that this software, when trained appropriately, can intensify law enforcement services by being accurate and structured. [Aasawari Boxey et.al., Journal of Image Processing and Intelligent Remote Sensing, 2022, no. 24, pp. 15-23, HM Publishers] proposed an approach that uses many Python libraries, such as NumPy and OpenCV, to enable the proposed system to produce error-free conclusions, even in the face of changing light circumstances associated with distinct locations or areas.

Traditional search methods lack efficiency and security in today's fast-paced society. Manual search processes are time-consuming and prone to errors, while existing face recognition systems often rely on centralized databases, raising privacy concerns.
This invention provides Mini Seeker Robot with Face Recognition is a compact device designed to autonomously navigate and search environments while identifying and tracking individuals through facial recognition technology. It integrates advanced algorithms for real-time image processing and machine learning to accurately detect and match faces against a database. Equipped with mobility features like wheels or tracks, it can explore various terrains efficiently. This solution promises enhanced security and efficient search operations in scenarios ranging from surveillance to search and rescue missions.
Detailed of Prior Art
In the current decade, ongoing developments in face recognition technology include improved accuracy, better resistance to variations in lighting and pose, and increased attention to ethical considerations. Some regions and countries have implemented or considered regulations to address privacy concerns and establish guidelines for the responsible use of facial recognition technology. The history of face recognition reflects a continuous evolution from early research to sophisticated, deep learning-based approaches. As the technology advances, discussions about privacy, security, and ethical considerations continue to shape its deployment and regulation.The emergence of a compact search robot integrating the ESP32-CAM with IoT capabilities represents a significant technological advancement with broad implications across diverse domains. This pioneering robot seamlessly integrates a compact design, real-time visual data capture, and IoT-enabled remote control, making it an indispensable and versatile tool applicable across various scenarios, such as search and rescue missions, industrial inspections, and surveillance operations. With the ESP32-CAM integration, the robot gains the capability to transmit and receive visual data, offering operators crucial insights into areas that are otherwise inaccessible or confined. Furthermore, integrating IoT capabilities facilitates remote monitoring, control, and data analysis, enhancing the robot's autonomy and adaptability. This cutting-edge solution not only improves operational efficiency but also reduces human exposure to risks in hazardous environments, while simultaneously driving advancements in automation and remote exploration.
This patent involves designing of an IoT Face Recognition Robot which can recognize and track the human face using APP and GPS. The face recognition is processed by the algorithm based on Python 3 (with the OpenCV library) by using Raspberry Pi. Ultrasonic sensor was interfaced successfully to get the robot runs automatically otherwise its runs the manually by using APP Control.The devised surveillance robot system, equipped with facial recognition capabilities, diligently monitors its surrounding environment. Upon detecting objects, the robot navigates around them and continues its patrol. Continuously vigilant, the system not only identifies individuals but also determines whether they are recognized or unknown. Furthermore, it offers live image streaming and alert message functionalities, rendering it well-suited for deployment in war zones or border surveillance operations. Furthermore, the system is adept at identifying any suspicious objects carried by individuals within high-security zones (US7369686B2).

Summary of Invention
This invention is about the mini seeker robot with face recognition stands out from competitors thanks to its advanced AI algorithms, which enable lightning-fast recognition and unparalleled accuracy. Its compact design allows for seamless integration into various environments, while its intuitive user interface ensures effortless interaction. Additionally, our focus on privacy and security ensures user data is handled with the utmost care, setting us apart in the market.
Detailed description of the invention
Keeping up one's international defining borders has grown to be an extremely difficult undertaking for any nation. Personnel in charge of border security may not always be able to keep an eye on lengthy boundaries. Robotic technology deployment is crucial in the current geopolitical environment to detect border invaders and send information back to the control center. While it can be dangerous for fighters, robots are better suited for performing many high-risk tasks in hazardous environments. The suggested endeavor aims to develop an automatic technique for detecting the presence of any opponent or hostile entity. "MINI SEEKER" is a robotic vehicle designed to spy on a pre-designated area by continuously monitoring it. Prior to doing a surveillance system check, the system checks for human presence.
Classifiers are employed to classify images into two distinct terms i.ie., positive (i.e., presence of a face) or negative (i.e., absence of a face). This allows for the process of face detection. Large collections of positive (face-containing) and negative (face-free) images are used to train these classifiers. Pre-trained classifiers are available in OpenCV, such as the Haar Classifier, Face recognition algorithm and the Object Classification algorithm .
Haar Classifier-By making use of the "Integral Image" principle, the Haar classifier allows for quick feature computing for the detector. Based on the Adaboost principle, the algorithm generates effective classifiers by choosing a subset of important features from a large pool. Combining intricate classifiers creates a "cascade." This cascade system effectively recognizes face regions in an image while rejecting any non-facial portions.
Face recognition algorithm-For face recognition, the Local Binary Patterns Histogram (LBPH) technique is employed. This method involves generating local binary patterns for each pixel in the image. Essentially, it examines the intensity relationship between a central pixel and its surrounding neighbours. If the intensity of the central pixel is equal to or greater than that of its neighbours, it is encoded as '1'; otherwise, it is encoded as '0'. Afterward, every binary pattern undergoes conversion into its corresponding decimal number, which is then used to generate a histogram depicting the distribution of these decimal values.
Object Classification algorithm-Deep neural networks fall into two primary categories: detection networks and base networks. The suggested method extracts high-level characteristics for both detection and classification by using the Mobile Net as the basis network. Through the Single Shot Detector (SSD) algorithm in detection network, the convolutional layers are used on top of the base network to perform detection tasks.

We utilized the cost-effective Raspberry Pi model 4 for this facial recognition invention, which boasts extensive support for Python face recognition libraries. We employed libraries like face recognition and OpenCV, which incorporates the Haar cascade classifier as its detection algorithm. The hardware setup comprised a 5V power supply, a 2GB SD card for operating system installation, and a 5MP Pi camera V2 module connected to Raspberry Pi model 4+ via the CSI port. Raspberry Pi OS Buster (Legacy) was selected as the operating system for this project. The operating system was installed on a microSD card using the Raspberry Pi Imager software (headless mode) and connected to the Raspberry Pi via a microSD card adapter. The Raspberry Pi, a compact minicomputer, has a unique IP address like any other device. The Raspberry Pi's IP address was ascertained, and an SSH connection was established to access the terminal. A desktop instance was created using VNC software.
For facial recognition, we utilized OpenCV with Python 3.6 in the Anaconda Spyder environment for development and debugging. The recognition system was implemented on the Raspberry Pi Board. The 'face recognition' library was employed for face detection, while the 'pyttsx3' library enabled speech output upon face recognition, accompanied by GPS location data. The 'requests' library and Integromat webhook were integrated to store user data in a database.

Mini seeker consists of sensors and motor activators that are interfaced with a controller. The face detection system is constructed in OpenCV Python programming using the Haar Classifier technique, and the robot's facial recognition functionality is demonstrated. Upon facial recognition in the database, the name and image of the detected individual are sent to Telegram, together with the position of the device at that moment. This robot also keeps on sensing the objects in front of it by the help of ultrasonic sensor in a loop and when there is an object ahead it lets the user know through the buzzer and overcomes it by changing its direction.
This invention involves designing of an IoT Face Recognition Robot which can recognize and track the human face using APP and GPS. The face recognition is processed by the algorithm based on Python 3 (with the OpenCV library) by using Raspberry Pi. Ultrasonic sensor was interfaced successfully to get the robot runs automatically otherwise its runs the manually by using APP Control.
The devised surveillance robot system, equipped with facial recognition capabilities, diligently monitors its surrounding environment. Upon detecting objects, the robot navigates around them and continues its patrol. Continuously vigilant, the system not only identifies individuals but also determines whether they are recognized or unknown. Furthermore, it offers live image streaming and alert message functionalities, rendering it well-suited for deployment in war zones or border surveillance operations. Furthermore, the system is adept at identifying any suspicious objects carried by individuals within high-security zones.

Brief description of Drawing
Figure 1 Block diagram of mini seeker robot
Figure 2 Flowchart of Mini Seeker Robot
Figure 3 Creation of a little seeker robot prototype

Detailed description of the drawing
Figure 1 shows “Mini Seeker Robot”, is developed with the help of the controller named Raspberry Pi and its numerous control functions are implemented using OpenCV-Python programming language. Using an L293D motor driver module and Raspberry Pi General Purpose Input Output (GPIO) control signals, DC motors are driven via an H-bridge. GPIO pins on the Raspberry Pi are used to send signals from the sensor module. The board comes with the 12 V power supply which powers all gear. Using the time difference between the emitted and reflected waves, the ultrasonic sensor calculates the distance between itself and the obstruction. Additionally, a USB connector is used to interface a Pi Camera with a Raspberry Pi. This robot system with the help of an Android application can be manually operated.
MINI SEEKER ROBOT” comprises a robotic vehicle that continuously monitors the pre-allocated area to spy on it along with facial recognition algorithm whenever a person is seen. The technology identifies the person and sends their location to the Telegram app via a GPS module. Robot controlled both automatically by an ultrasonic sensor and manually by an APP. Additionally, the system uses a Raspberry Pi and VNC Viewer to broadcast live surveillance data to the operator in Figure 2.

Figure 3 shows the creation of a little seeker robot prototype that has all of its features illustrated. It consists of sensors and motor activators that are interfaced with a controller. The face detection system is constructed in OpenCV Python programming using the Haar Classifier technique, and the robot's facial recognition functionality is demonstrated. Upon facial recognition in the database, the name and image of the detected individual are sent to Telegram, together with the position of the device at that moment. , Claims:The scope of the invention is defined by the following claims:
Claims:
1. The Mini Seeker Robot with Face Recognition is a compact device designed to autonomously navigate and search environments while identifying and tracking individuals through facial recognition technology:
a) A integrated advanced algorithms for real-time image processing and machine learning is used to accurately detect and match faces against a database
b) A mini seeker robot with face recognition addresses these issues by combining the speed of automated search with the accuracy of facial recognition technology
c) The compact design allows for seamless integration into various environments, while its intuitive user interface ensures effortless interaction
2. As claimed in Claim 1, employing an Instantly identifying individuals, it streamlines tasks by providing personalized assistance, enhancing security, and optimizing workflow
3. As claimed in Claim 1, mini seeker compact size ensures mobility, making it ideal for various settings from homes to offices, delivering unparalleled convenience and peace of mind.