SYMBOLIC FACTORIAL DISCRIMINANT ANALYSIS FOR ILLUMINATION INVARIANT FACE RECOGNITION

2008 ◽  
Vol 22 (03) ◽  
pp. 371-387 ◽  
Author(s):  
P. S. HIREMATH ◽  
C. J. PRABHAKAR
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Minimum Distance ◽  
Face Representation ◽  
Lighting Conditions ◽  
Illumination Changes ◽  
Effective Performance ◽  
Interval Type ◽  
Human Faces ◽  
Factorial Discriminant Analysis

In this paper, a new appearance-based technique called symbolic factorial discriminant analysis (symbolic FDA) is explored for face representation and recognition under varying illumination conditions. In the past few years, many appearance-based methods have been proposed to model image variations of human faces under different lighting conditions using single valued variables to represent the facial features. In the proposed symbolic factorial discriminant analysis method, we extract interval type discriminating features, which are robust to illumination changes. The minimum distance classifier with symbolic dissimilarity measure is used for classification. The proposed method has been successfully tested for face recognition using three databases, namely, Yale Face database B, CMU PIE database and Harvard database. The experimental results have demonstrated the effective performance of this method.

Analysis of Face Space for Recognition using Interval-Valued Subspace Technique

2012 ◽  
pp. 108-127
Author(s):  
C.J. Prabhakar
Keyword(s):  
Face Recognition ◽  
Research Work ◽  
Recognition Algorithm ◽  
Fisher Discriminant Analysis ◽  
Face Representation ◽  
Face Space ◽  
Interval Type ◽  
Facial Expression Classification ◽  
Expression Classification ◽  
Interval Valued

The major contribution of the research work presented in this chapter is the development of effective face recognition algorithm using analysis of face space in the interval-valued subspace. The analysis of face images is used for various purposes such as facial expression classification, gender determination, age estimation, emotion assessment, face recognition, et cetera. The research community of face image analysis has developed many techniques for face recognition; one of the successful techniques is based on subspace analysis. In the first part of the chapter, the authors present discussion of earliest face recognition techniques, which are considered as mile stones in the roadmap of subspace based face recognition techniques. The second part presents one of the efficient interval-valued subspace techniques, namely, symbolic Kernel Fisher Discriminant analysis (Symbolic KFD), in which the interval type features are extracted in contrast to classical subspace based techniques where single valued features are used for face representation and recognition.

Algorithm Study of Face Recognition on Improved 2DLDA

2011 ◽  
Vol 128-129 ◽  
pp. 58-61
Author(s):  
Shi Ping Li ◽  
Yu Cheng ◽  
Hui Bin Liu ◽  
Lin Mu
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Small Sample ◽  
New Method ◽  
Scatter Matrix ◽  
Linear Discriminant ◽  
Face Representation ◽  
Paired Class ◽  
The Relationship

Linear Discriminant Analysis (LDA) [1] is a well-known method for face recognition in feature extraction and dimension reduction. To solve the “small sample” effect of LDA, Two-Dimensional Linear Discriminant Analysis (2DLDA) [2] has been used for face recognition recently，but its could hardly take use of the relationship between the adjacent scatter matrix. In this paper, I improved the between-class scatter matrix, proposed paired-class scatter matrix for face representation and recognition. In this new method, a paired between-class scatter matrix distance metric is used to measure the distance between random paired between-class scatter matrix. To test this new method, ORL face database is used and the results show that the paired between-class scatter matrix based 2DLDA method （N2DLDA） outperforms the 2DLDA method and achieves higher classification accuracy than the 2DLDA algorithm.

LBP-like feature based on Gabor wavelets for face recognition

2017 ◽  
Vol 15 (05) ◽  
pp. 1750049 ◽  
Author(s):  
Wen-Juan Li ◽  
Jun Wang ◽  
Zheng-Hai Huang ◽  
Ting Zhang ◽  
Daniel K. Du
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Gabor Transform ◽  
Georgia Tech ◽  
Feature Extraction Method ◽  
Face Representation ◽  
Illumination Changes ◽  
Feature Based ◽  
Robust Feature Extraction ◽  
New Feature

The robust feature extraction method for face representation is an important issue in face recognition. In this paper, we extract a new kind of feature through applying the idea of local binary pattern (LBP) into the resulted sub-images of Gabor transform. The new feature, i.e. Gabor-LBP-Like (GLLBP), together with its extension methods (1) overcome the drawback of losing information after Gabor transform’s down-sampling; (2) are insensitive to noise, compared with the LBP feature extracted from the original face image; and (3) are robust to image variation, especially occlusion and illumination changes when compared with other existing features combined LBP and Gabor transform. To validate the effectiveness of these features, we do experiments on the ORL, FERET, Georgia Tech and LFW facial databases. The numerical results show that GLLBP and its extensions are miraculous features for face recognition.

scholarly journals A modification of line Hausdorff distance for face recognition to reduce computational cost

2017 ◽  
Vol 20 (K3) ◽  
pp. 152-158 ◽  
Author(s):  
Chau Nguyen Dang ◽  
Tuan Hong Do
Keyword(s):  
Pattern Recognition ◽  
Face Recognition ◽  
Hausdorff Distance ◽  
Computational Cost ◽  
Ideal Condition ◽  
Modern Life ◽  
Significant Challenge ◽  
Face Expression ◽  
Lighting Conditions ◽  
Human Faces

Face recognition, that has a lot of applications in modern life, is still an attractive research for pattern recognition community. Due to the similarity of human faces, face recognition presents a significant challenge for pattern recognition researchers. Hausdorff distance is an efficient parameter for measuring the similarity between objects. Line Hausdorff distance (LHD) technique, which is the applying of Hausdorff distance for face recognition, gives high accuracy in comparing with common methods for face recognition. For fast screen techniques such as LHD, the computational cost is a key issue. A modified Line Hausdorff distance (MLHD) is proposed in this paper. The performance of the proposed method is compared with LHD method for face recognition in various conditions: 1) ideal condition of face, 2) varying lighting conditions, 3) varying poses and 4) varying face expression. It is very encouraging that the proposed method gives lower computational cost than LHD while keeping the accuracy of face recognition equal to the LHD method.

scholarly journals CHALLENGES IN FACE RECOGNITION TECHNIQUE

2021 ◽  
Vol 23 (07) ◽  
pp. 1201-1204
Author(s):  
Milan. M. P ◽  
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Face Detection ◽  
Detection Rate ◽  
Difficult Problem ◽  
Lighting Conditions ◽  
Age Variations ◽  
Human Faces ◽  
Unconstrained Face ◽  
Recognition Technique

Face detection is an application that is able of detecting, track, and recognizing human faces from an angle or video captured by a camera. A lot of advances have been made up in the domain of face recognition for security, identification, and appearance purpose, but still, difficult to able to beat humans alike accuracy. There are various problems in human facial presence such as; lighting conditions, image noise, scale, presentation, etc. Unconstrained face detection remains a difficult problem due to intra-class variations acquired by occlusion, disguise, capricious orientations, facial expressions, age variations…etc. The detection rate of face recognition algorithms is actually low in these conditions. With the popularity of AI in recent years, a mass number of enterprises deployed AI algorithms in absolute life settings. it is complete that face patterns observed by robots depend generally on variations such as pose, light environment, location.

scholarly journals View-tuned and view-invariant face encoding in IT cortex is explained by selected natural image fragments

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yunjun Nam ◽  
Takayuki Sato ◽  
Go Uchida ◽  
Ekaterina Malakhova ◽  
Shimon Ullman ◽  
...  
Keyword(s):  
Face Recognition ◽  
Visual Pathway ◽  
Visual Features ◽  
Natural Image ◽  
Face Representation ◽  
Low Level ◽  
Ventral Visual Pathway ◽  
Neural Substrate ◽  
The Face

AbstractHumans recognize individual faces regardless of variation in the facial view. The view-tuned face neurons in the inferior temporal (IT) cortex are regarded as the neural substrate for view-invariant face recognition. This study approximated visual features encoded by these neurons as combinations of local orientations and colors, originated from natural image fragments. The resultant features reproduced the preference of these neurons to particular facial views. We also found that faces of one identity were separable from the faces of other identities in a space where each axis represented one of these features. These results suggested that view-invariant face representation was established by combining view sensitive visual features. The face representation with these features suggested that, with respect to view-invariant face representation, the seemingly complex and deeply layered ventral visual pathway can be approximated via a shallow network, comprised of layers of low-level processing for local orientations and colors (V1/V2-level) and the layers which detect particular sets of low-level elements derived from natural image fragments (IT-level).

The Adaboost Algorithm Applied in ATM Automatic Identification System

2013 ◽  
Vol 753-755 ◽  
pp. 2941-2944
Author(s):  
Ming Hui Zhang ◽  
Yao Yu Zhang
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Computing Time ◽  
Automatic Identification ◽  
Identification System ◽  
Human Face ◽  
Detection Experiment ◽  
Adaboost Algorithm ◽  
Face Features ◽  
Human Faces

Seeing that human face features are unique, an increasing number of face recognition algorithms on existing ATM are proposed. Since face detection is a primary link of face recognition, our system adopts AdaBoost algorithm which is based on face detection. Experiment results demonstrated that the computing time of face detection using this algorithm is about 70ms, and the single and multiple human faces can be effectively measured under well environment, which meets the demand of the system.

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