Relaxed local ternary pattern for face recognition

Local binary pattern (LBP) is sensitive to noise. Local ternary pattern (LTP) partially solves this problem by encoding the small pixel difference into a third state. The small pixel difference may be easily overwhelmed by noise. Thus, it is difficult to precisely determine its sign and magnitude. In this paper, we propose the concept of uncertain state to encode the small pixel difference. We do not care its sign and magnitude, and encode it as both 0 and 1 with equal probability. The proposed Relaxed LTP is tested on the CMU-PIE database, the extended Yale B database and the O2FN mobile face database. Superior performance is demonstrated compared with LBP and LTP.

[1]  Xudong Jiang,et al.  Eigenfeature Regularization and Extraction in Face Recognition , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[3]  Xudong Jiang,et al.  Complete discriminant evaluation and feature extraction in kernel space for face recognition , 2008, Machine Vision and Applications.

[4]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[6]  Loris Nanni,et al.  Local binary patterns variants as texture descriptors for medical image analysis , 2010, Artif. Intell. Medicine.

[7]  Moulay A. Akhloufi,et al.  Locally adaptive texture features for multispectral face recognition , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[8]  David J. Kriegman,et al.  Acquiring linear subspaces for face recognition under variable lighting , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Cong Geng,et al.  Face recognition based on the multi-scale local image structures , 2011, Pattern Recognit..

[10]  Xudong Jiang,et al.  A complete and fully automated face verification system on mobile devices , 2013, Pattern Recognit..

[11]  Wen-Hung Liao,et al.  Texture Classification Using Uniform Extended Local Ternary Patterns , 2010, 2010 IEEE International Symposium on Multimedia.

[12]  Nick Cercone,et al.  Local Triplet Pattern for Content-Based Image Retrieval , 2009, ICIAR.