Comparative Study of Face Recognition Techniques

International Journal of Engineering Research and Technology (IJERT), 2014

https://www.ijert.org/comparative-study-of-face-recognition-techniques https://www.ijert.org/research/comparative-study-of-face-recognition-techniques-IJERTV3IS030687.pdf The aim of this paper is to compare various methods for face recognition. In this paper we are going to discuss various techniques for face recognition. This paper includes a brief description of PCA (Principle Component Analysis), LDA (Linear Discriminant Analysis), LBP (Local Binary Pattern), Gabor filter, LGBP (Local Gabor Binary Pattern). Comparative study of various methods is discussed in this paper and from this study a suitable and more efficient technique is found out which will give more effective result.

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2019

Face recognition has been a quickly developing, testing and intriguing region progressively applications. An extensive number of face recognition calculations have been produced in a decade ago. In this paper an endeavor is made to survey an extensive variety of techniques utilized for face recognition exhaustively. This incorporate PCA, LDA, ICA, SVM, Gabor wavelet delicate registering instrument like ANN for recognition and different cross breed blend of this systems. This audit examines every one of these techniques with parameters that difficulties face recognition like illumination, pose variation, facial expressions.

Face recognition has been a fast growing, challenging and interesting area in real time applications. A large number of face recognition algorithms have been developed in last decades. In this paper an attempt is made to review a wide range of methods used for face recognition comprehensively. This include PCA, LDA, ICA, SVM, Gabor wavelet soft computing tool like ANN for recognition and various hybrid combination of this techniques. This review investigates all these methods with parameters that challenges face recognition like illumination, pose variation, facial expressions.

Face recognition has remained a competitive and huge area of research in image processing system. This paper discusses major two approaches, first is featured based and second is statistical .Former comprises of Scale Space Filtering, Elastic Bunch Graph and the later one includes PCA and LDA techniques for recognition. This review provides a brief comparative study between the features of face recognition techniques and also discussed the performance under different circumstances.

2014

In this review paper, different algorithms of Face Recognition have been presented. There are different types of algorithms which can be used for Face Recognition that are PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), ICA (Independent Component Analysis), EBGM (Elastic Bunch Graph Matching), Fisherfaces. We have also studied techniques which use different kind of approaches to develop Face Recognition System using PCA. Some of them use Neural Network, Eigenface and Artificial Neural Network, etc. with PCA. This paper provides comparison between face recognition algorithms and the combination of PCA with different techniques and in last their merits and demerits.

International Journal of Multidisciplinary Research and Explorer (IJMRE), 2021

Department of computer science engineering Jd college of engineering and management Kalmeshwar road valni Nagpur 441501. Abstract-The most significant part of face recognition is the input representation. This refers to the transformation of the intensity map to a form of input representation that allows easy and effective extraction of highly discriminative features. The next stage is classification. Although this is an important stage, the popular techniques and their strengths are fairly similar to each other. As such, the choice of classification algorithm does not affect the recognition accuracy as much as input representation. Input representation is the major factor that differentiate face recognition algorithms. It can be approached in 2 manners: a geometrical approach that uses spatial configuration of the facial feature, and a more pictorial approach that uses image-based representation. In this paper a set of different face recognition algorithms are reviewed, and the best practices in this domain are studied and verified.

NFC-IEFR Journal of Engineering and Scientific Research, 2016

In the field of computer sciences such as graphics and also analyzing the image and its processing, face recognition is the most prominent problem due to the comprehensive variation of faces and the complexity of noises and image backgrounds. The purpose and working of this system is that it identifies the face of a person from the real time video and verifies the person from the images store in the database. This paper provides a review of the methodologies and techniques used for face detection and recognition. Firstly a brief introduction of Facial Recognition is given then the review of the face recognition's working which has been done until now, is briefly introduced. Then the next sections covered the approaches, methodologies, techniques and their comparison. Holistic, Feature based and Hybrid approaches are basically used for face recognition methodologies. Eigen Faces, Fisher Faces and LBP methodologies were introduced for recognition purpose. Eigen Faces is most frequently used because of its efficiencies. To observe the efficient techniques of facial recognition, there are many scenarios to measure its performance which are based on real time.

— Face recognition has remained a competitive and huge area of research in image processing system. This paper discusses major two approaches, first is featured based and second is statistical .Former comprises of Scale Space Filtering, Elastic Bunch Graph and the later one includes PCA and LDA techniques for recognition. This review explores with gaussian and Gabor filter used for edge detection of image in feature based approach and PCA is used Eigen face for recognition. Keywords: Elastic Bunch Graph Matching (EBGM) Scale Space Filtering (SSF), Principal Component Analysis (PCA), Linear Discriminate Analysis (LDA).

International Journal of Computer Applications, 2015

In recent years, the biometrics has achieved a great attention on a world level. A Biometric System operates by getting biometric information from a personal that extracts a feature set from the data which is acquired, and helps in comparing this feature set against the template stored in the database. There are biometric technologies which could either be physiological or behavioral. Face Recognition is having the importance to provide biometric authentication with easy image acquisition that can be used for online and offline applications. There are number of existing approaches for biometric facial recognition and classification. This paper gives a review on some of the common and reliable approaches which include PCA, LDA, SVM, SIFT, SURF, etc.

2018

Face Recognition which is still one of the challenging topic in Computer Vision and Image Processing field remains an open problem as the recent advancements has not yet reached high recognition performance in real world environment. With the usage of technologies for Computer Vision and Image Processing, Face Recognition has gained more interest due to it applications and concerns on high security. Human Face can be considered as a key identifier in various fields and Computational models of face recognition can be applied to a wide variety of problems involving security system, Identification of criminals or suspects, image and film processing, and human computer interaction. This field of computer vision and image processing involves recognition of face from image or a video source. Several algorithms and methodologies for face identification have been developed having their own pros and cons. In this paper, we will provide review and survey of some famous major face recognition ...