face detection
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Shrey Bhagat

Abstract: Face recognition systems are used in practically every industry in this digital age. One of the most widely utilized biometrics is face recognition. It can be used for security, authentication, and identity, among other things. Despite its low accuracy relative to iris and fingerprint identification, it is extensively utilized because it is a contactless and non-invasive technique. Face recognition systems can also be used to track attendance in schools, colleges, and companies. Because the existing manual attendance system is time consuming and difficult to maintain, this system intends to create a class attendance system that employs the concept of face recognition. There’s also the possibility of proxy attendance. As a result, the demand for this system grows. Database development, face detection, face recognition, and attendance updating are the four steps of this system. The photos of the kids in class are used to generate the database. Faces are discovered and recognized from the classroom's live streaming footage. At the end of the session, the attendance will be mailed to the appropriate faculty. Keywords: Smart Attendance System, NFC, RFID, OpenCV, NumPy

Prof. Kalpana Malpe

Abstract: In recent years, the safety constitutes the foremost necessary section of the human life. At this point, the price is that the greatest issue. This technique is incredibly helpful for reducing the price of watching the movement from outside. During this paper, a period of time recognition system is planned which will equip for handling pictures terribly quickly. The most objective of this paper is to safeguard home, workplace by recognizing individuals. The face is that the foremost distinctivea part of human’s body. So, it will replicate several emotions of associate degree Expression. A few years past, humans were mistreatment the non-living things like good cards, plastic cards, PINS, tokens and keys for authentication, and to urge grant access in restricted areas like ISRO, National Aeronautics and Space Administration and DRDO. The most necessary options of the face image are Eyes, Nose and mouth. Face detection and recognition system is simpler, cheaper, a lot of accurate, process. The system under two categories one is face detection and face recognition. Throughout this case, among the paper, the Raspberry Pi single-board computer is also a heart of the embedded face recognition system. Keywords: Raspberry Pi, Face recognition system

Prof. Hemant B. Shinde

The Online Examination Portal is a web application for taking an online test productively along with face recognition capabilities to perform live proctoring, and there is no time wasted for checking the paper. This report will incorporate all highlights and procedures which are required to develop this portal. This document incorporates details about the objective of the system, approximately targets of the system, system scope confinement, essential system requirements, group advancement, likely venture risks, schedule of the deployment, and finally observing and reporting mechanisms for the whole system. Online Examination Conducting Portal is exceptionally useful for Instructive Institute's to prepare a complete exam, conduct proctoring to prevent misconduct, secure the time that will take to check the paper, and plan check sheets. Online Examination Portal will help the Institutes to test understudies and develop their abilities. But the impediments for the Online Exam systems, it takes more time when the user prepares the exam at the primary time for utilization. To conduct the exam we require the number of computers with the same number of students. With the successful use of the Examination Portal, the facilitator can utilize this system to create the tests as their requirements and we can get accurate results and save time once deployed.

Yashwanth D

Automatic Face Detection innovations have made numerous upgrades in evolving world. Brilliant ATTENDANCE SYSTEM utilizing ongoing face acknowledgment is a genuine world arrangement which accompanies everyday exercises of taking care of understudies participation. The administration of participation framework can be an extraordinary weight on educators in case it is finished by hands.To determine this issue we utilize auto and brilliant participation framework which is by and large executed with the assistance of biometric called Face Detection. The primary execution steps utilized in this kind of framework are face location and perceiving the identified countenances. Face Detection is an interaction where the framework will actually want to recognize the human faces which will be caught by the camera. Here , we execute a computerized participation the board framework for understudies of the class by utilizing face acknowledgment method..

2022 ◽  
Vol 6 (1) ◽  
pp. 9
Dweepna Garg ◽  
Priyanka Jain ◽  
Ketan Kotecha ◽  
Parth Goel ◽  
Vijayakumar Varadarajan

In recent years, face detection has achieved considerable attention in the field of computer vision using traditional machine learning techniques and deep learning techniques. Deep learning is used to build the most recent and powerful face detection algorithms. However, partial face detection still remains to achieve remarkable performance. Partial faces are occluded due to hair, hat, glasses, hands, mobile phones, and side-angle-captured images. Fewer facial features can be identified from such images. In this paper, we present a deep convolutional neural network face detection method using the anchor boxes section strategy. We limited the number of anchor boxes and scales and chose only relevant to the face shape. The proposed model was trained and tested on a popular and challenging face detection benchmark dataset, i.e., Face Detection Dataset and Benchmark (FDDB), and can also detect partially covered faces with better accuracy and precision. Extensive experiments were performed, with evaluation metrics including accuracy, precision, recall, F1 score, inference time, and FPS. The results show that the proposed model is able to detect the face in the image, including occluded features, more precisely than other state-of-the-art approaches, achieving 94.8% accuracy and 98.7% precision on the FDDB dataset at 21 frames per second (FPS).

Shilpa Sharma ◽  
Linesh Raja ◽  
Vaibhav Bhatnagar ◽  
Divya Sharma ◽  
Swami Nisha Bhagirath ◽  

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Jiangjin Gao ◽  
Tao Yang

The existing face detection methods were affected by the network model structure used. Most of the face recognition methods had low recognition rate of face key point features due to many parameters and large amount of calculation. In order to improve the recognition accuracy and detection speed of face key points, a real-time face key point detection algorithm based on attention mechanism was proposed in this paper. Due to the multiscale characteristics of face key point features, the deep convolution network model was adopted, the attention module was added to the VGG network structure, the feature enhancement module and feature fusion module were combined to improve the shallow feature representation ability of VGG, and the cascade attention mechanism was used to improve the deep feature representation ability. Experiments showed that the proposed algorithm not only can effectively realize face key point recognition but also has better recognition accuracy and detection speed than other similar methods. This method can provide some theoretical basis and technical support for face detection in complex environment.

2022 ◽  
Hang Du ◽  
Hailin Shi ◽  
Dan Zeng ◽  
Xiao-Ping Zhang ◽  
Tao Mei

Face recognition is one of the most popular and long-standing topics in computer vision. With the recent development of deep learning techniques and large-scale datasets, deep face recognition has made remarkable progress and been widely used in many real-world applications. Given a natural image or video frame as input, an end-to-end deep face recognition system outputs the face feature for recognition. To achieve this, a typical end-to-end system is built with three key elements: face detection, face alignment, and face representation. The face detection locates faces in the image or frame. Then, the face alignment is proceeded to calibrate the faces to the canonical view and crop them with a normalized pixel size. Finally, in the stage of face representation, the discriminative features are extracted from the aligned face for recognition. Nowadays, all of the three elements are fulfilled by the technique of deep convolutional neural network. In this survey article, we present a comprehensive review about the recent advance of each element of the end-to-end deep face recognition, since the thriving deep learning techniques have greatly improved the capability of them. To start with, we present an overview of the end-to-end deep face recognition. Then, we review the advance of each element, respectively, covering many aspects such as the to-date algorithm designs, evaluation metrics, datasets, performance comparison, existing challenges, and promising directions for future research. Also, we provide a detailed discussion about the effect of each element on its subsequent elements and the holistic system. Through this survey, we wish to bring contributions in two aspects: first, readers can conveniently identify the methods which are quite strong-baseline style in the subcategory for further exploration; second, one can also employ suitable methods for establishing a state-of-the-art end-to-end face recognition system from scratch.

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