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

Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-61350-429-1.ch007 ◽

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.

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FACE RECOGNITION BASED ON GPPBTF AND LBP WITH CLASSIFIER FUSION

International Journal of Image and Graphics ◽

10.1142/s0219467812500118 ◽

2012 ◽

Vol 12 (02) ◽

pp. 1250011

Author(s):

GANG XU ◽

HUCHUAN LU ◽

ZUNYI WANG

Keyword(s):

Face Recognition ◽

Texture Feature ◽

Texture Features ◽

Classifier Fusion ◽

Fisher Discriminant Analysis ◽

Face Representation ◽

Fisher Discriminant ◽

Kernel Fisher Discriminant Analysis ◽

Robust Face Recognition ◽

Combination Approach

Robust face recognition is a challenging problem, due to facial appearance variations in illumination, pose, expression, aging, partial occlusions and other changes. This paper proposes a novel face recognition approach, where face images are represented by Gabor pixel-pattern-based texture feature (GPPBTF) and local binary pattern (LBP), and null pace-based kernel Fisher discriminant analysis (NKFDA) is applied to the two features independently to obtain two recognition results which are eventually combined together for a final identification. To get GPPBTF, we first transform an image into Gabor magnitude maps of different orientations and scales, and then use pixel-pattern-based texture feature to extract texture features from Gabor maps. In order to improve the final performance of the classification, this paper proposes a multiple NKFDA classifiers combination approach. Extensive experiments on FERET face database demonstrate that the proposed method not only greatly reduces the dimensionality of face representation, but also achieves more robust result and higher recognition accuracy.

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FUSING GLOBAL AND LOCAL COMPLETE LINEAR DISCRIMINANT FEATURES BY FUZZY INTEGRAL FOR FACE RECOGNITION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001408006806 ◽

2008 ◽

Vol 22 (07) ◽

pp. 1427-1445 ◽

Cited By ~ 2

Author(s):

CHENGYUAN ZHANG ◽

QIUQI RUAN ◽

YI JIN

Keyword(s):

Face Recognition ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Null Space ◽

Information Aggregation ◽

Recognition Algorithm ◽

Fuzzy Integral ◽

Fisher Discriminant Analysis ◽

Linear Discriminant ◽

Global And Local

Face recognition becomes very difficult in a complex environment, and the combination of multiple classifiers is a good solution to this problem. A novel face recognition algorithm GLCFDA-FI is proposed in this paper, which fuses the complementary information extracted by complete linear discriminant analysis from the global and local features of a face to improve the performance. The Choquet fuzzy integral is used as the fusing tool due to its suitable properties for information aggregation. Experiments are carried out on the CAS-PEAL-R1 database, the Harvard database and the FERET database to demonstrate the effectiveness of the proposed method. Results also indicate that the proposed method GLCFDA-FI outperforms five other commonly used algorithms — namely, Fisherfaces, null space-based linear discriminant analysis (NLDA), cascaded-LDA, kernel-Fisher discriminant analysis (KFDA), and null-space based KFDA (NKFDA).

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Effective Method of Age Dependent Face Recognition

International Journal of Computer Science and Informatics ◽

10.47893/ijcsi.2011.1022 ◽

2011 ◽

pp. 125-129

Author(s):

Hlaing Htake Khaung Tin ◽

Myint Myint Sein

Keyword(s):

Face Recognition ◽

Age Groups ◽

Recognition System ◽

Input Image ◽

Face Database ◽

Face Representation ◽

Face Space ◽

Age Dependent ◽

The Face ◽

Age Prediction

This Automatic age dependent face recognition system is developed. This approach is based on the Principle Component Analysis (PCA). Eigen face approach is used for both age prediction and face recognition. Face database is created by aging groups individually. The age prediction is carried out by projecting a new face image into this face space and then comparing its position in the face space with those of known faces. After that we find the best match in the related face database, the Eigen face representation of an input image is first obtained. Then it is compared with the Eigen face representation of face in the database. The closest one is the match. It will be reduced the time complexity using this approach. The proposed method preserves the identity of the subject while enforcing a realistic recognition effects on adult facial images between 15 to 70 years old. The accuracy of the system is analyzed by the variation on the range of the age groups. The efficiency of the system can be confirmed through the experimental results.

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Symmetry, probability, and recognition in face space

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.0812680106 ◽

2009 ◽

Vol 106 (17) ◽

pp. 6895-6899 ◽

Cited By ~ 25

Author(s):

Lawrence Sirovich ◽

Marsha Meytlis

Keyword(s):

Face Recognition ◽

Probability Distribution ◽

Recognition Algorithm ◽

Face Space ◽

Human Faces

The essential midline symmetry of human faces is shown to play a key role in facial coding and recognition. This also has deep and important connections with recent explorations of the organization of primate cortex, as well as human psychophysical experiments. Evidence is presented that the dimension of face recognition space for human faces is dramatically lower than previous estimates. One result of the present development is the construction of a probability distribution in face space that produces an interesting and realistic range of (synthetic) faces. Another is a recognition algorithm that by reasonable criteria is nearly 100% accurate.

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Facial Composite System Using Real Facial Features

Research Papers Faculty of Materials Science and Technology Slovak University of Technology ◽

10.2478/rput-2014-0029 ◽

2014 ◽

Vol 22 (35) ◽

pp. 9-15

Author(s):

Soňa Duchovičová ◽

Barbora Zahradníková ◽

Peter Schreiber

Keyword(s):

Face Recognition ◽

Face Detection ◽

Composite System ◽

Facial Feature ◽

Facial Features ◽

Feature Points ◽

Facial Image ◽

Facial Expression Classification ◽

Expression Classification ◽

Facial Composite

Abstract Facial feature points identification plays an important role in many facial image applications, like face detection, face recognition, facial expression classification, etc. This paper describes the early stages of the research in the field of evolving a facial composite, primarily the main steps of face detection and facial features extraction. Technological issues are identified and possible strategies to solve some of the problems are proposed.

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SYMBOLIC FACTORIAL DISCRIMINANT ANALYSIS FOR ILLUMINATION INVARIANT FACE RECOGNITION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800140800634x ◽

2008 ◽

Vol 22 (03) ◽

pp. 371-387 ◽

Cited By ~ 8

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.

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