Introduction to face recognition and evaluation of algorithm performance

Face Recognition is an efficient technique and one of the most liked biometric software application for the identification and verification of specific individual in a digital image by analysing and comparing patterns. This paper presents a survey on well-known techniques of face recognition. The primary goal of this review is to observe the performance of different face recognition algorithms such as SVM (Support Vector Machine), CNN (Convolutional Neural Network), Eigenface based algorithm, Gabor Wavelet, PCA (Principle Component Analysis) and HMM (Hidden Markov Model). It presents comparative analysis about the efficiency of each algorithm. This paper also figure out about various face recognition applications used in real world and face recognition challenges like Illumination Variation, Pose Variation, Occlusion, Expressions Variation, Low Resolution and Ageing in brief. Another interesting component covered in this paper is review of datasets available for face recognition. So, must needed survey of many recently introduced face recognition aspects and algorithms are presented.

Attendance marking in a classroom during a lecture is not only a onerous task but also a time consuming one at that. Due to an unusually high number of students present during the lecture there will always be a probability of proxy attendance(s).Attendance marking with conventional methods has been an area of challenge. The growing need of efficient and automatic techniques of marking attendance is a growing challenge in the area of face recognition. In recent years, the problem of automatic attendance marking has been widely addressed through the use of standard biometrics like fingerprint and Radio frequency Identification tags etc., However,these techniques lack the element of reliability. In this proposed project an automated attendance marking and management system is proposed by making use of face detection and recognition algorithms. Instead of using the conventional methods, this proposed system aims to develop an automated system that records the student’s attendance by using facial recognition technology. The main objective of this work is to make the attendance marking and management system efficient, time saving, simple and easy. Here faces will be recognized using face recognition algorithms. The processed image will then be compared against the existing stored record and then attendance is marked in the database accordingly. Compared to existing system traditional attendance marking system, this system reduces the workload of people. This proposed system will be implemented with 4 phases such as Image Capturing, Segmentation of group image and Face Detection, Face comparison and Recognition, Updating of Attendance in database.

The last decade has seen automatic face recognition evolve from small-scale research systems to a wide range of commercial products. Driven by the FERET face database and evaluation protocol, the currently best commercial systems achieve verification accuracies comparable to those of fingerprint recognizers. In these experiments, only frontal face images taken under controlled lighting conditions were used. As the use of face recognition systems expands toward less restricted environments, the development of algorithms for view and illumination invariant face recognition becomes important. However, the performance of current algorithms degrades significantly when tested across pose and illumination, as documented in a number of evaluations. In this chapter, we review previously proposed algorithms for pose and illumination invariant face recognition. We then describe in detail two successful appearance-based algorithms for face recognition across pose, eigen light-fields, and Bayesian face subregions. We furthermore show how both of these algorithms can be extended toward face recognition across pose and illumination.

In this paper, face recognition algorithms under three low-quality conditions, namely, complex illumination, low resolution and partial occlusion, are summarized, and relatively new face recognition algorithms under low-quality conditions are introduced and explained. Such as the algorithm of face recognition with occlusion based on partition, face recognition algorithm based on Iteratively Reweighted Robust Principal Component and face recognition with occlusion based on MEBML, illumination face recognition based on RPCA and Convolutional Neural Network, face recognition based on Illumination Normalization and Block-based Adaptive Local Ternary Pattern, low-resolution face recognition based on blocking CS-LBP and weighted PCA algorithm, pose Robust low-resolution face recognition via CKEDA and other face recognition algorithm under low-quality conditions. The existing problems and development trend of face recognition under low quality conditions are analyzed.

In this paper, it studies two critical technical links of face detection and face recognition in the recognition of face, and conducts analyses and comparisons on some commonly used face detections and recognition algorithms. Through the collation of this information and the combination of related knowledge in the digital image processing, it proposes a face recognition algorithm based on the singular value decomposition. After preprocessing face images, by the use of the projection method, it obtains positions of five sense organs, and then extracts local feature values near five sense organs as the main features of faces by using the singular value decomposition. For different face images of the same individual, the matching degree of feature values will be very high.KeywordsFace recognitionimage recognitionlocal informationfeature extraction

Facial recognition is a feature that uses facial detection algorithms to detect a face and then invokes facial recognition algorithms to try to match the person’s face. First, a person’s face needs to be enrolled; during enrollment, the person’s facial details are saved in a database—in this case, a mobile phone. The facial recognition algorithm uses this database to match the currently presented face with the faces saved in the database. The efficiency of a facial recognition algorithm depends upon the speed at which it can detect and recognize faces. The problem we address in this paper is that a reliable, automated method for testing facial recognition features in mobile phones. Because the multimedia capabilities of smartphones have expanded phenomenally, the need for thoroughly testing facial recognition algorithms has become crucial. The uses of facial recognition can range from facial authentication for unlocking phones to security for a number of other applications. To meet these needs, a reliable, automated test for validating the facial recognition algorithms must be developed. The challenge for the software test team was to automate the test cases, which involve tilting the phone at specific angles from the test subject. The permissible angular movements of the phone are determined by the algorithmic specifications of the facial recognition algorithm. We tested scenarios involving multiple faces, motion blur, panning the phone in front of the test subject faces at various speeds, and so on. We adopted a robotic arm to perform the facial recognition test cases and developed software to program the robotic arm to test the phone’s facial recognition software for functionality, performance, and stability as well as adversarial tests. This paper discusses the computer vision use case for facial recognition and describes how we developed an automated facial recognition test suite using a robotic arm.

Critical to any automated surveillance system is the recognition component. Since the inception of Pattern Recognition & Image processing, researchers across the globe continue to propose newer facial recognition algorithms. Each of these facial algorithms has its own pros and cons. A typical facial recognition algorithm consists of three steps namely: facial detection, feature extraction and recognition. In this paper we discuss serial and parallel implementation of four standard facial recognition algorithms namely, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA) and FISHER Face. Work presented in this paper consists of two sections. In section 1, we discuss serial and parallel implementation of the algorithms. Section 2, presents a comparative performance analysis of the above 4 algorithms. A major challenge in this effort is the ability to explore and exploit concurrency in existing facial recognition algorithms to improve performance. Based on the algorithms performance, we have made few suggestions regarding the suitability of each of these algorithms for different domains/applications. All algorithms are implemented using open source OPENCV and OPENMP libraries.

The sparse representation based classification (SRC) can effectively improve the face recognition rate. Smoothed l 0 algorithm has much faster calculation speed and requires fewer measured values than the other sparse representation method. In this paper, the sparse representation and smoothed l 0 algorithm for face recognition are presented to improve the face recognition under various conditions such as face disguise, illumination and pose changes, etc. The experiments on the AR, Extended Yale B and FERET face database verify the effectiveness of the presented method. The experimental results show that the face recognition algorithm increases to a certain extent in terms of recognition robustness and time than the original SRC.

Facial images are of critical importance in many real-world applications from gaming to surveillance. The current literature on facial image analysis, from face detection to face and facial expression recognition, are mainly performed in either RGB, Depth (D), or both of these modalities. But, such analyzes have rarely included Thermal (T) modality. This paper paves the way for performing such facial analyzes using synchronized RGB-D-T facial images by introducing a database of 51 persons including facial images of different rotations, illuminations, and expressions. Furthermore, a face recognition algorithm has been developed to use these images. The experimental results show that face recognition using such three modalities provides better results compared to face recognition in any of such modalities in most of the cases.

Face recognition is one of the biometric identification methods with the highest potential. The existing face recognition algorithms relying on the texture information of face images are affected greatly by the variation of expression, scale and illumination. Whereas the algorithms based on the shape topology weaken the influence of illumination to some extent, but the impact of expression, scale and illumination on face recognition is still unsolved. To this end, we propose a new method for face recognition by integrating texture information with shape information, called biview face recognition algorithm. The texture models are constructed by using subspace learning methods and shape topologies are formed by building graphs for face images. The proposed biview face recognition method is compared with recognition algorithms merely based on texture or shape information. Experimental results of recognizing faces under the variation of illumination, expression and scale demonstrate that the performance of the proposed biview face recognition outperforms texture-based and shape-based algorithms.

An empirical study of the impact of masks on face recognition

The first step in an automatic face recognition system is to localize the face region in a cluttered background and carefully segment the face from each frame of a video sequence. In this paper, we propose a fast and efficient algorithm for segmenting a face suitable for recognition from a video sequence. The cluttered background is first subtracted from each frame, in the foreground regions a coarse face region is found using skin colour. Then using a dynamic template matching approach the face is efficiently segmented. The proposed algorithm is invariant to large scale and pose variations. The segmented face is then handed over to a recognition algorithm based on principal component analysis and linear discriminant analysis. The on-line face detection, segmentation and recognition algorithm takes an average of 0.06 sec on a 3.2 GHz P4 machine.

Traditional attendance systems, such as manual sign-ins or RFID-based methods, are time- consuming, prone to errors, and susceptible to manipulation. This paper presents a Smart Attendance System that leverages facial recognition technology to automate attendance tracking. The system integrates advanced face detection and recognition algorithms, along with anti-spoofing mechanisms, to ensure secure and efficient attendance marking. The proposed system uses the OpenCV library for face detection, the FaceNet algorithm for facial recognition, and Fourier Spectrum Analysis for anti-spoofing. Built with Python and Tkinter, the system provides a user-friendly interface for real-time attendance logging and management. The results demonstrate high accuracy in face recognition, efficient processing, and robust security against spoofing attempts, making it a reliable solution for modern attendance management in educational and corporate environments.

In this work we propose a Live Attendance Marking System for institutional purpose. This system will enable the department to mark the attendance of students automatically by recognizing their faces. The system is based on the face detection and recognition algorithms and automatically recognizes a student whenever he/she comes across the camera module. Further after the recognition, it automatically updates his/her attendance in the Cloud web server database. The basic architecture and all the algorithms used in the model are described in the paper elaborately. Moreover in order to evaluate and enhance the performance of the system many of the filter functions are used which provide the capability to capture and recognize the images even in dull and low light places. This project also suggests the technique to ensure that a student can mark the attendance only once in a day. The live attendance system is much more efficient to traditional attendance systems both in saving time and in maintaining the database.

In the traditional system, it is difficult to handle the attendance of a huge number of students in the classroom. Attendance of the students is very important for every university, college as well as school as it keeps the record of every individual. The conventional methodology of taking attendance by calling out the roll number or name of the student and then recording the attendance is a tedious task. Assume the duration of the lecture is 60 minutes or 1 hour and to record the attendance it would take 5 to 10 minutes. Also for every tutor, this is time-consuming. To overcome this problem, real-time face recognition is a real-world solution, which will help in marking the attendance of students in bulk. In this project, face detection and face recognition are used. Attendance marking through face recognition can be implemented in the classroom by capturing the image of students in the classroom via a surveillance camera. Later Haar Cascade algorithm is used for face detection, which helps in locating the position of the face region, and LBPH (Local binary pattern histogram) algorithm for face recognition. The images of all the students in the class are stored in the database and when the face of the individual student matches with one of the faces stored in the database then the attendance is recorded