Abstract: To solve the problem of low feature representation capacity and discriminality of collaborative representation based classifier(CRC) and related algorithms in face recognition,a locality constrained feature line representation based classifier(LCFLRC) is proposed for face recognition.At first,LCFLRC represents a test image as a linear combination of the projections of the test image on the overall feature lines,and a constraint with respect to the distance between the test image and each feature line is imposed.Then,the L₂norm based optimization problem is solved by using the Lagrange multiplier method.At last,the label of the test image is decided according to the reconstruction residual between the test image and the projections of the test image on the feature lines of each class.LCFLRC could capture more variations iin facial images by using the feature lines to represent the test image,and contains more discriminant information by taking advantage of the distance information between the test image and feature line such that the reconstruction coefficient of the projection on the feature line nearer to the test image is larger.Experimental results on CMU PIE,Extended Yale-B and AR face databases demonstrate that the proposed method significantly outperform other classification methods with varying illumination,facial expressions and poses in images.

Key words: Face recognition, Feature classification, Feature line representation, Locality constrained