Block independent component analysis for face recognition

Lei Zhang, Quanxue Gao, Dapeng Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

12 Citations (Scopus)

Abstract

This paper presents a subspace algorithm called block independent component analysis (B-ICA) for face recognition. Unlike the traditional ICA, in which the whole face image is stretched into a vector before calculating the independent components (ICs), B-ICA partitions the facial images into blocks and takes the block as the training vector. Since the dimensionality of the training vector in B-ICA is much smaller than that in traditional ICA, it can reduce the face recognition error caused by the dilemma in ICA, i.e. the number of available training samples is greatly less than that of the dimension of training vector. Experiments on the well-known Yale and AR databases validate that the B-ICA can achieve higher recognition accuracy than ICA and enhanced ICA (EICA).
Original languageEnglish
Title of host publicationProceedings - 14th International Conference on Image Analysis and Processing, ICIAP 2007
Pages217-222
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2007
Event14th Edition of the International Conference on Image Analysis and Processing, ICIAP 2007 - Modena, Italy
Duration: 10 Sep 200714 Sep 2007

Conference

Conference14th Edition of the International Conference on Image Analysis and Processing, ICIAP 2007
CountryItaly
CityModena
Period10/09/0714/09/07

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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