Proceedings ArticleDOI
Face recognition using subspaces techniques
G. Prabhu Teja,S. Ravi +1 more
- pp 103-107
TLDR
By applying a range of image processing techniques it is demonstrated that the performance is highly dependent on the type of pre-processing steps used and that Equal Error Rates of the Eigenface and Fisherface methods can be reduced using the method proposed in this paper.Abstract:
With many applications in various domains, Face Recognition technology has received a great deal of attention over the decades in the field of image analysis and computer vision. It has been studied by scientists from different areas of psychophysical sciences and those from different areas of computer science. Psychologists and neuro-scientists mainly deal with the human perception part of the topic where as engineers studying on machine recognition of human faces deal with the computational aspects of Face Recognition. Face Recognition is an important and natural human ability of a human being. However developing a computer algorithm to do the same thing is one of the toughest tasks in computer vision. Research over the past several years enables similar recognitions automatically. Various face recognition techniques are represented through various classifications such as, Image-based face recognition and Video-based recognition, Appearance-based and Model-based, 2D and 3D face recognition methods. This paper gives a review of different face recognition techniques available as of today. The focus is on subspace techniques, investigating the use of image pre-processing applied as a preliminary step in order to reduce error rates. The Principle Component Analysis, Linear Discriminant Analysis and their modified methods of face recognition are implemented under subspace techniques, computing False Acceptance Rates (FAR)and False Rejection Rates (FRR) on a standard test set of images that pose typical difficulties for recognition. By applying a range of image processing techniques it is demonstrated that the performance is highly dependent on the type of pre-processing steps used and that Equal Error Rates (EER) of the Eigenface and Fisherface methods can be reduced using the method proposed in this paper.read more
Citations
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Journal ArticleDOI
2D-3D Face Recognition Method Basedon a Modified CCA-PCA Algorithm
TL;DR: A 2D-3D face-matching method based on a principal component analysis (PCA) algorithm using canonical correlation analysis (CCA) to learn the mapping between a 2D face image and 3D face data and results are shown that the classification and recognition results based on the modified CCA-PCA method are superior to thosebased on the CCA method.
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TL;DR: Certain approaches for face recognition by using feature extraction are discussed along with the issues and the recommendations as to which approach is better and suitable.
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Blind subjects faces database
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Interval Type-2 Fuzzy Logic to the Treatment of Uncertainty in 2D Face Recognition Systems
Saad M. Darwish,Ali H. Mohammed +1 more
TL;DR: This paper deals with the design of intelligent 2D face recognition system using interval type-2 fuzzy logic for diminishing the effects of uncertainty formed by variations in light direction, face pose and facial expression.
References
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Journal ArticleDOI
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Journal ArticleDOI
Face recognition: A literature survey
TL;DR: In this paper, the authors provide an up-to-date critical survey of still-and video-based face recognition research, and provide some insights into the studies of machine recognition of faces.
Journal ArticleDOI
Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions
Xiaoyang Tan,Bill Triggs +1 more
TL;DR: This work presents a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition, and improves robustness by adding Kernel principal component analysis (PCA) feature extraction and incorporating rich local appearance cues from two complementary sources.
Journal ArticleDOI
Face recognition by independent component analysis
TL;DR: Independent component analysis (ICA), a generalization of PCA, was used, using a version of ICA derived from the principle of optimal information transfer through sigmoidal neurons, which was superior to representations based on PCA for recognizing faces across days and changes in expression.
Journal ArticleDOI
Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition
Chengjun Liu,Harry Wechsler +1 more
TL;DR: Zhang et al. as discussed by the authors introduced a novel Gabor-Fisher (1936) classifier (GFC) for face recognition, which is robust to changes in illumination and facial expression, applies the enhanced Fisher linear discriminant model (EFM) to an augmented Gabor feature vector derived from the Gabor wavelet representation of face images.