Fast Haar transform based feature extraction for face representation and recognition

Authors:
Yanwei Pang;Xuelong Li;Yuan Yuan;Dacheng Tao;Jing Pan
Affiliations:
School of Electronic Information Engineering, Tianjin University, Tianjin, China;State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi, China;School of Engineering and Applied Science, Aston University, Birmingham, UK;School of Computer Engineering, Nanyang Technological University, Singapore, Singapore;Electronic Engineering Department, Tianjin University of Technology and Education, Tianjin, China
Venue:
IEEE Transactions on Information Forensics and Security
Year:
2009

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Abstract

Subspace learning is the process of finding a proper feature subspace and then projecting high-dimensional data onto the learned low-dimensional subspace. The projection operation requires many floating-point multiplications and additions, which makes the projection process computationally expensive. To tackle this problem, this paper proposes two simple-but-effective fast subspace learning and image projection methods, fast Haar transform (FHT) based principal component analysis and FHT based spectral regression discriminant analysis. The advantages of these two methods result from employing both the FHT for subspace learning and the integral vector for feature extraction. Experimental results on three face databases demonstrated their effectiveness and efficiency.