Face Recognition Systems: A Survey

Each And Every Face Databases Have Its Own Limitations And Description To Test The Performance Of Face Recognition Technique. Even Though, Current Machine Recognition Systems Have Approached To Certain Level Of Recognition Rate. Their Success Is Limited By The Conditions Imposed By Many Real Applications And Constraint Imposed On Databases Such As Recognition Of Faces Acquired In An Outdoor Environment With Changes Of Illumination And Or Poses Remains A Largely Unsolved Problem. In View Of These This Paper Discusses Most Of The Video Databases And Face Recognition Methods. The Performance Of The Existing Methods On Video Datasets, NRC-IIT Video Data Set , The Honda/UCSD Video Data Set ,Choke Point And Mcgill Face Dataset Are Studied, Compare Their Performance In Terms Of Recognition Rate And Conclusion Reveals Future Development Of The Work.

ArXiv, 2022

Face Recognition has proven to be one of the most successful technology and has impacted heterogeneous domains. Deep learning has proven to be the most successful at computer vision tasks because of its convolution-based architecture. Since the advent of deep learning, face recognition technology has had a substantial increase in its accuracy. In this paper, some of the most impactful face recognition systems were surveyed. Firstly, the paper gives an overview of a general face recognition system. Secondly, the survey covers various network architectures and training losses that have had a substantial impact. Finally, the paper talks about various databases that are used to evaluate the capabilities of a face recognition system.

2012

In recent days, the need of biometric security system is heightened for providing safety and security against terrorist attacks, robbery, etc. The demand of biometric system has risen due to its strength, efficiency and easy availability. One of the most effective, highly authenticated and easily adaptable biometric security systems is facial feature recognition. This paper h a s covered almost all the techniques for face recognition approaches. It also covers the relative analysis between all the approaches which are useful in face recognition. Consideration of merits and demerits of all techniques is done and recognition rates of all the techniques are also compared.

2020

Face recognition is one of the most suitable applications of image analysis. It’s a true challenge to build an automated system which equals human ability to recognize faces. While traditional face recognition is typically based on still images, face recognition from video sequences has become popular recently due to more abundant information than still images. Video-based face recognition has been one of the hot topics in the field of pattern recognition in the last few decades. This paper presents an overview of face recognition scenarios and video-based face recognition system architecture and various approaches are used in video-based face recognition system which can not only discover more space-time semantic information hidden in video face sequence, but also make full use of the high level semantic concepts and the intrinsic nonlinear structure information to extract discriminative manifold features. We also compare our algorithm with other algorithms on our own database.

Acm Computing Surveys …, 2003

2014

Abstract: Face recognition has been in spot light for last few decades by keeping in view its increasing usage in real world applications, still challenges are there to meet, especially in real world applications. A lot of work has been reported on face recognition during the recent decades, some of which have also come up with their modifications. This paper presents a detailed analysis on the importance and application of face recognition technology, mentioning the factors affecting its applicability in real life. In addition, several face recognition techniques along with their experimental results are also discussed. The issues that still need to be addressed are mentioned here as well. Key words: Face, Recognition, Biometrics, Survey, Holistic INTRODUCTION Face recognition technology[1] has been considered seriously by the researchers in the past few years keeping in view its escalating usage in law enforcement and commercial applications[2; 3]. This technology is the result of...

Journal of Information Processing Systems, 2009

The development of face recognition methods for unconstrained environments is a challenging problem. The aim of this work is to carry out a comparative study of existing face recognition methods that are suitable to work properly in these environments. The analyzed methods are selected by considering their performance in former comparative studies, in addition to be real-time, to require just one image per person, and to be fully online (no requirements of offline enrollment). The methods are compared using the LFW database, which was built to evaluate face recognition methods in real-world conditions. The results of this comparative study are intended to be a guide for developers of face recognition systems.

Face recognition is considered as one of the emerging topics in computer vision application. Currently biometric research is mainly focused on automated face recognition in various applications such as surveillance application in various public security measures. Face recognition is a biometric system used to categorize or authenticate a person from a digital image by extracting its features and then distinguishing it, regardless external conditions. This learning version has mainly two motives: first one is the extensive assortment of profitable and regulation implementation claims, second is the convenience of possible skills. This paper is organised as three sections. The first section describes brief introduction to face recognition. The second section types of face recognition and various problems affecting the process and finally third section describes a comparative study of different face recognition methods.

International Journal of Research, 2017

As a standout amongst the best uses of picture examination and comprehension, face acknowledgment has as of late gotten huge consideration, particularly amid the past quite a long while. No less than two reasons represent this pattern: the first is the extensive variety of business and law authorization applications, and the second is the accessibility of achievable advancements following 30 years of exploration. Despite the fact that present machine acknowledgment frameworks have achieved a specific level of development, their prosperity is constrained by the conditions forced by numerous genuine applications. For instance, acknowledgment of face pictures procured in an open-air environment with changes in light and/or posture remains a great extent unsolved issue. At the end of the day, current frameworks are still far away from the ability of the human discernment framework. This paper presents a survey up to present state of the art techniques for face recognition.