Improving face recognition performance using similarity feature-based selection and classification algorithm

In recent years, the binary coding of face image features, such as local binary patterns (LBP) and local ternary patterns (LTP) have become popular in face recognition systems. These local feature descriptors provide a simple and powerful means for texture description. In this paper, we present a novel approach, which uses these descriptors to represent face images, and a similarity feature-based selection and classification algorithm to improve recognition rate. The face image is first divided into small regions from which LBP and LTP histograms are extracted and concatenated into a single feature vector. The proposed algorithm is used to select the similarity features of training set and classify the face image. The experiments are conducted on the ORL Database of Faces and the Extended Yale Face Database B. The results clearly show the superiority of the proposed algorithm.

[1]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[3]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[4]  Shouyi Yin,et al.  A High Precision Feature Based on LBP and Gabor Theory for Face Recognition , 2013, Sensors.

[5]  Xudong Jiang,et al.  Relaxed local ternary pattern for face recognition , 2013, 2013 IEEE International Conference on Image Processing.

[6]  Md Jan Nordin,et al.  Combining Local Binary Pattern and Principal Component Analysis on T-Zone face area for face recognition , 2011, 2011 International Conference on Pattern Analysis and Intelligence Robotics.

[7]  Rama Chellappa,et al.  Human and machine recognition of faces: a survey , 1995, Proc. IEEE.

[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]  Jianying Zhang,et al.  Face Recognition Based on Local Binary Patterns with Threshold , 2010, 2010 IEEE International Conference on Granular Computing.

[10]  Daijin Kim,et al.  MMI-Based Optimal LBP Code Selection for Face Recognition , 2009, 2009 11th IEEE International Symposium on Multimedia.

[11]  Di Huang,et al.  Local Binary Patterns and Its Application to Facial Image Analysis: A Survey , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[12]  Tsair-Fwu Lee,et al.  Improving Face Recognition Performance Using Similarity Feature-Based Selection and Classification Algorithm , 2013, 2013 Second International Conference on Robot, Vision and Signal Processing.

[13]  Wen-Hung Liao.  Region Description Using Extended Local Ternary Patterns , 2010, 2010 20th International Conference on Pattern Recognition.

[14]  Md. Rafiqul Islam,et al.  Face Recognition Using Local Binary Patterns (LBP) , 2013 .

[15]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[16]  Matti Pietikäinen,et al.  Face Recognition with Local Binary Patterns , 2004, ECCV.

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