Face recognition by subspace analysis of 2D Log-Gabor wavelets features

Despite the variety of approaches and tools studied, face recognition is not accurate or robust enough to be used in uncontrolled environments. Recently, infrared (IR) imagery of human faces is considered as a promising alternative to visible imagery. IR face recognition is a biometric which offers the security of fingerprints with the convenience of face recognition. However, IR has its own limitations. The presence of eyeglasses has more influence on IR than visible imagery. In this paper, a method based on Log-Gabor wavelets for IR face recognition is proposed. The method first derives a Log-Gabor feature vector from IR face image, then obtains the independent Log-Gabor features by using independent component analysis (ICA). Experimental results show that the proposed method works well, even in challenging situations.

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Face recognition method based on multi-channel Log-Gabor and (2D)2PCALDA

Journal of Computer Applications ◽

10.3724/sp.j.1087.2010.02970 ◽

2010 ◽

Vol 30 (11) ◽

pp. 2970-2973

Author(s):

Yuan-lian HUO

Keyword(s):

Face Recognition ◽

Recognition Method ◽

Log Gabor

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Intrapersonal subspace analysis with application to adaptive Bayesian face recognition

Pattern Recognition ◽

10.1016/j.patcog.2004.08.015 ◽

2005 ◽

Vol 38 (4) ◽

pp. 617-621 ◽

Cited By ~ 6

Author(s):

Liwei Wang ◽

Xiao Wang ◽

Jufu Feng

Keyword(s):

Face Recognition ◽

Subspace Analysis

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Saliency detection based on 2D log-gabor wavelets and center bias

Proceedings of the international conference on Multimedia - MM '10 ◽

10.1145/1873951.1874128 ◽

2010 ◽

Cited By ~ 5

Author(s):

Min Wang ◽

Jia Li ◽

Tiejun Huang ◽

Yonghong Tian ◽

Lingyu Duan ◽

...

Keyword(s):

Saliency Detection ◽

Gabor Wavelets ◽

Center Bias ◽

Log Gabor

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Tensor Independent Component Analysis and Tensor Non-Negative Factorization

Advances in Information and Communication Technology Education - Advanced Pattern Recognition Technologies with Applications to Biometrics ◽

10.4018/978-1-60566-200-8.ch010 ◽

2011 ◽

pp. 202-225

Author(s):

David Zhang ◽

Fengxi Song ◽

Yong Xu ◽

Zhizhen Liang

Keyword(s):

Face Recognition ◽

Independent Component Analysis ◽

Component Analysis ◽

Independent Component ◽

Biometric Recognition ◽

Subspace Analysis ◽

Potential Applications

In this chapter, we describe two tensor-based subspace analysis approaches (tensor ICA and tensor NMF) that can be used in many fields like face recognition and other biometric recognition. Section 10.1 gives the background and development of the two tensor-based subspace analysis approaches. Section 10.2 introduces tensor independent component analysis. Section 10.3 presents tensor nonnegative factorization. Section 10.4 discusses some potential applications of these two subspace analysis approaches in biometrics. Finally, we summarize this chapter in Section 10.5.

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Face Recognition and Semantic Features

Semantic Mining Technologies for Multimedia Databases ◽

10.4018/978-1-60566-188-9.ch003 ◽

2011 ◽

pp. 80-98

Author(s):

Huiyu Zhou ◽

Yuan Yuan ◽

Chunmei Shi

Keyword(s):

Face Recognition ◽

Singular Value Decomposition ◽

Eigenvalue Problem ◽

Singular Value ◽

Semantic Features ◽

Subspace Analysis ◽

Geometrical Properties ◽

The Face ◽

Tensor Subspace ◽

Value Decomposition

The authors present a face recognition scheme based on semantic features’ extraction from faces and tensor subspace analysis. These semantic features consist of eyes and mouth, plus the region outlined by three weight centres of the edges of these features. The extracted features are compared over images in tensor subspace domain. Singular value decomposition is used to solve the eigenvalue problem and to project the geometrical properties to the face manifold. They compare the performance of the proposed scheme with that of other established techniques, where the results demonstrate the superiority of the proposed method.

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KERNEL SUPERVISED DISCRIMINANT PROJECTION AND ITS APPLICATION FOR FACE RECOGNITION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800141356003x ◽

2013 ◽

Vol 27 (02) ◽

pp. 1356003 ◽

Cited By ~ 3

Author(s):

JIANGUO WANG

Keyword(s):

Face Recognition ◽

Experimental Results ◽

Subspace Method ◽

Subspace Analysis ◽

Face Images ◽

Discriminant Projection ◽

Kernel Mapping ◽

Discriminant Information

Subspace analysis is an effective approach for face recognition. In this paper, a novel subspace method, called kernel supervised discriminant projection (KSDP), is proposed for face recognition. In the proposed method, not only discriminant information with intrinsic geometric relations is preserved in subspace, but also complex nonlinear variations of face images are represented by nonlinear kernel mapping. Extensive experiments are performed to test and evaluate the new algorithm. Experimental results on three popular benchmark databases, FERET, Yale and AR, demonstrate the effectiveness of the proposed method, KSDP.

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