Face recognition using the feature fusion technique based on LNMF and NNSC algorithms

Authors:
Li Shang;Changxiong Zhou;Yunian Gu;Yu Zhang
Affiliations:
JiangSu Province Support Software Engineering R&D Center for Modern Information Technology Application in Enterprise, Suzhou, China and Department of Electronic Information Engineering, Suzhou ...;JiangSu Province Support Software Engineering R&D Center for Modern Information Technology Application in Enterprise, Suzhou, China and Department of Electronic Information Engineering, Suzhou ...;JiangSu Province Support Software Engineering R&D Center for Modern Information Technology Application in Enterprise, Suzhou, China;JiangSu Province Support Software Engineering R&D Center for Modern Information Technology Application in Enterprise, Suzhou, China
Venue:
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Year:
2010

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Abstract

A new face recognition method, realized by the feature fusion technique based on Local Non-negative Sparse Coding (NNSC) and Local Nonnegative Matrix Factorization (LNMF) algorithms, is proposed in this paper. NNSC and LNMF are both part-based representations of the multi-dimensional data, used widely and efficiently in image feature extraction and pattern recognition. Here, considered the high recognition rate, the weighting coefficient fusion method between features obtained by algorithms of NNSC and LNMF is discussed in the face recognition task. Using the distance classifier and the Radial Basis Probabilistic Neural Network (RBPNN) classifier, the recognition task is easily implemented on the ORL face database. Moreover, compared with any other algorithm of NNSC and LNMF, experimental results show that the feature fusion method is indeed efficient and applied in the face recognition.