Face Detection and Recognition for Automatic Attendance System
06 Sep 2018-pp 237-245
TL;DR: An Attendance System with Face Recognition has been developed which automatically tracks the attendance status of the students and performs daily activities of the attendance analysis which is an important aspect of face recognition task.
Abstract: Human face recognition is an important part of biometric verification. The methods for utilizing physical properties, such as human face have seen a great change since the emergence of image processing techniques. Human face recognition is widely used for verification purposes, especially if individuals attend to lectures. There is a lot of time lost in classical attendance confirmations. In order to solve this time loss, an Attendance System with Face Recognition has been developed which automatically tracks the attendance status of the students. The Attendance System with Face Recognition performs daily activities of the attendance analysis which is an important aspect of face recognition task. By doing this in an automated manner, it saves time and effort in classrooms and meetings. In the scope of the proposed system, a camera attached to the front of the classroom continuously captures the images of the students, detects the faces in the images, compares them with the database, and thus the participation of the student is determined. Haar filtered AdaBoost is used to detect the real-time human face. Principal Component Analysis (PCA) and Local Binary Pattern Histograms (LBPH) algorithms have been used to identify the faces detected. The paired face is then used to mark course attendance. By using the Attendance System with Facial Recognition, the efficiency of lecture times’ utilization will be improved. Additionally, it will be possible to eliminate mistakes on attendance sheets.
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TL;DR: In this article, a barcode-based student attendance system that can be easily accessed by lecturers, to help them to avoid maintaining the registry book, providing valuable information about the students and the reports can be generated using real-time processing.
Abstract: In view of the importance of students' attendance at lectures and their impact on their academic achievement, universities take the necessary measures to reduce excessive absenteeism. This is a highly important problem. The administration requires careful follow-up, taking care of it and not being lenient. At present, attendance and absence at the universities are recorded by calling the names of the students or by signing the student's attendance paper. In the process of admitting students into an examination hall in most KSA universities, 85% of attendance must be met and also considered for grade computation, therefore there is a huge need for monitoring and recording students’ attendance. The aim of this work is to design and implement a barcode based student attendance system that can be easily accessed by the lecturers, to help them to avoid maintaining the registry book, providing valuable information about the students and the reports can be generated using real-time processing. The proposed work was designed and implemented using the Unified Modeling Language (UML), Microsoft Access 2007 and ASP.NET programming language.
18 citations
01 Nov 2017
TL;DR: The face recognition system has been implemented to Social Robot which can recognize and tracking human face and then mentioned the person name and result shows a good accuracy for Human-Robot Interaction.
Abstract: This paper discusses the development of Social Robot named SyPEHUL (System of Physic, Electronic, Humanoid Robot and Machine Learning) which can recognize and tracking human face Face recognition and tracking process use Cascade Classification and LBPH (Local Binary Pattern Histogram) Face Recognizer method based on OpenCV library and Python 27 The social robot hardware based on Arduino microcontroller contains by 12 DoF (Degree of Freedom) motor servos to actuate robotic head and its face The face recognition system has been implemented to Social Robot which can recognize and tracking human face and then mentioned the person name The face recognition system of Social Robot result shows a good accuracy for Human-Robot Interaction
14 citations
Cites background from "Face Detection and Recognition for ..."
...In this decade, face recognition can be implemented to many projects are; social robot [14] [15], attendance automation [16], home security [17] [18] [19], game [5], and other....
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15 Mar 2019
TL;DR: An automated system that records the student’s attendance by using facial recognition technology for those who are present during lecture hours by using face detection and recognition algorithms is proposed.
Abstract: In proposed system an automated attendance marking and management system is proposed by using face detection and recognition algorithms. Identification of human faces by the unique characteristics or features of their face is known as Face recognition. Currently, Face recognition technology is the fastest growing technology. Instead of using the traditional methods, this proposed system aims to develop an automated system that records the student’s attendance by using facial recognition technology for those who are present during lecture hours. The main objective of this work is to make the attendance marking and management system fully automatic, simple and easy. In this work the facial recognition of face is done by image processing techniques. The processed image is used to match with the existing stored record and then attendance is marked in the database correspondingly. Compared to existing system traditional attendance marking system, this system reduces the workload of people and also saves times. This proposed system is been implemented with 4 modules such as Image Capturing, Segmentation of group photo and Face Detection, Face comparison and Recognition, Updating of Attendance in database.
13 citations
01 Nov 2018
TL;DR: This paper compares face recognition approach using the K-Nearest Neighbor (K-NN) algorithm and Artificial Neural Network (ANN) algorithm, which serve to classify each different person in a database.
Abstract: Face recognition is an identification system that uses face characteristics of each person to identify and differentiate a person from others. In utilizing the development of technology, face recognition has attracted much attention in the world of technology. It is used for many purposes, but most often for security and law enforcement purposes. For example, face recognition is used to prevent people from getting fake identification cards. Face recognition also can be used for entrants who will enter a particular place such as security post to help security in doing their job. However, security has limited ability to make sure that entrants are not a threat to others. In our working, face recognition is used in security post to prevent stranger people with no particular purpose come in. In this paper, we compare face recognition approach using the K-Nearest Neighbor (K-NN) algorithm and Artificial Neural Network (ANN) algorithm. K-NN and ANN serve to classify each different person in a database. The result presents K-NN has 44.101% of accuracy and ANN has 38.177% of accuracy.
10 citations
TL;DR: The proposed segmentation model based on the clustering algorithm, which is driven by the modified version of the Artificial Bee Colony evolutionary optimization, allows for dynamical tracking of the alcohol-temperature features within a process of intoxication, from the sober state up to the maximum observed intoxication level.
Abstract: Alcohol intoxication is a significant phenomenon, affecting many social areas, including work procedures or car driving. Alcohol causes certain side effects including changing the facial thermal distribution, which may enable the contactless identification and classification of alcohol-intoxicated people. We adopted a multiregional segmentation procedure to identify and classify symmetrical facial features, which reliably reflects the facial-temperature variations while subjects are drinking alcohol. Such a model can objectively track alcohol intoxication in the form of a facial temperature map. In our paper, we propose the segmentation model based on the clustering algorithm, which is driven by the modified version of the Artificial Bee Colony (ABC) evolutionary optimization with the goal of facial temperature features extraction from the IR (infrared radiation) images. This model allows for a definition of symmetric clusters, identifying facial temperature structures corresponding with intoxication. The ABC algorithm serves as an optimization process for an optimal cluster’s distribution to the clustering method the best approximate individual areas linked with gradual alcohol intoxication. In our analysis, we analyzed a set of twenty volunteers, who had IR images taken to reflect the process of alcohol intoxication. The proposed method was represented by multiregional segmentation, allowing for classification of the individual spatial temperature areas into segmentation classes. The proposed method, besides single IR image modelling, allows for dynamical tracking of the alcohol-temperature features within a process of intoxication, from the sober state up to the maximum observed intoxication level.
8 citations
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TL;DR: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features that is assessed in the face recognition problem under different challenges.
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5,563 citations
Book•
14 Dec 2016
TL;DR: Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book any developer or hobbyist needs to get started, with the help of hands-on exercises in each chapter.
Abstract: Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to "see" and make decisions based on that data.The second edition is updated to cover new features and changes in OpenCV 2.0, especially the C++ interface.Computer vision is everywherein security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. OpenCV provides an easy-to-use computer vision framework and a comprehensive library with more than 500 functions that can run vision code in real time. Whether you want to build simple or sophisticated vision applications, Learning OpenCV is the book any developer or hobbyist needs to get started, with the help of hands-on exercises in each chapter.This book includes:A thorough introduction to OpenCV Getting input from cameras Transforming images Segmenting images and shape matching Pattern recognition, including face detection Tracking and motion in 2 and 3 dimensions 3D reconstruction from stereo vision Machine learning algorithms
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TL;DR: This paper investigates gender recognition on real-life faces using the recently built database, the Labeled Faces in the Wild (LFW), and local Binary Patterns (LBP) is employed to describe faces, and Adaboost is used to select the discriminative LBP features.
359 citations
01 Jul 2017
TL;DR: This NAN is trained with a standard classification or verification loss without any extra supervision signal, and it is found that it automatically learns to advocate high-quality face images while repelling low-quality ones such as blurred, occluded and improperly exposed faces.
Abstract: This paper presents a Neural Aggregation Network (NAN) for video face recognition. The network takes a face video or face image set of a person with a variable number of face images as its input, and produces a compact, fixed-dimension feature representation for recognition. The whole network is composed of two modules. The feature embedding module is a deep Convolutional Neural Network (CNN) which maps each face image to a feature vector. The aggregation module consists of two attention blocks which adaptively aggregate the feature vectors to form a single feature inside the convex hull spanned by them. Due to the attention mechanism, the aggregation is invariant to the image order. Our NAN is trained with a standard classification or verification loss without any extra supervision signal, and we found that it automatically learns to advocate high-quality face images while repelling low-quality ones such as blurred, occluded and improperly exposed faces. The experiments on IJB-A, YouTube Face, Celebrity-1000 video face recognition benchmarks show that it consistently outperforms naive aggregation methods and achieves the state-of-the-art accuracy.
323 citations