Occluded face recognition based on the improved SVM and block weighted LBP

Facial occlusions, for example, sunglasses, and scarves, etc., can significantly affect the performance of any facial recognition system. The focus of this paper is on facial occlusions, and particularly, on how to improve the recognition of faces occluded by sunglasses and scarves. We propose a new approach that consists of first detecting the presence of sunglasses/scarves and then processing the non-occluded facial regions only. The occlusion detection problem is approached using PCA and improved support vector machines (SVM), while the recognition of the non-occluded facial part is performed using blocked-based weighted local binary patterns (LBP).

[1]  Marian Stewart Bartlett,et al.  Face recognition by independent component analysis , 2002, IEEE Trans. Neural Networks.

[2]  Aleix M. Martínez,et al.  Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Sang Uk Lee,et al.  Occlusion invariant face recognition using selective local non-negative matrix factorization basis images , 2008, Image Vis. Comput..

[4]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[5]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[6]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[8]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Penio S. Penev,et al.  Local feature analysis: A general statistical theory for object representation , 1996 .

[10]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[11]  A. Martínez,et al.  The AR face databasae , 1998 .

[12]  Jongsun Kim,et al.  Effective representation using ICA for face recognition robust to local distortion and partial occlusion , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.