Low-Resolution Face Recognition of Multi-Scale Blocking CS-LBP and Weighted PCA

A novel method is proposed in this paper to improve the recognition accuracy of Local Binary Pattern (LBP) on low-resolution face recognition. More precise descriptors and effectively face features can be extracted by combining multi-scale blocking center symmetric local binary pattern (CS-LBP) based on Gaussian pyramids and weighted principal component analysis (PCA) on low-resolution condition. Firstly, the features statistical histograms of face images are calculated by multi-scale blocking CS-LBP operator. Secondly, the stronger classification and lower dimension features can be got by applying weighted PCA algorithm. Finally, the different classifiers are used to select the optimal classification categories of low-resolution face set and calculate the recognition rate. The results in the ORL human face databases show that recognition rate can get 89.38% when the resolution of face image drops to 12×10 pixel and basically satisfy the practical requirements of recognition. The further comparison of oth...

[1]  Huanxi Liu,et al.  A Correlative Two-Step Approach to Hallucinating Faces , 2014, Int. J. Pattern Recognit. Artif. Intell..

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

[3]  Xuelong Li,et al.  A Comprehensive Survey to Face Hallucination , 2013, International Journal of Computer Vision.

[4]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Roman V. Yampolskiy,et al.  Wavelet-Based Multiscale Adaptive LBP with Directional Statistical Features for Recognizing Artificial Faces , 2012, ISRN Machine Vision.

[6]  Yong Man Ro,et al.  Local color vector binary pattern for face recognition , 2011, 2011 18th IEEE International Conference on Image Processing.

[7]  Yiming Zhang,et al.  Coupled marginal discriminant mappings for low-resolution face recognition , 2015 .

[8]  Zhiguo Niu,et al.  Facial expression recognition based on weighted principal component analysis and support vector machines , 2010, 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE).

[9]  M. Pietikäinen,et al.  A discriminative feature space for detecting and recognizing faces , 2004, CVPR 2004.

[10]  Ethem Alpaydin,et al.  An Incremental Framework Based on Cross-Validation for Estimating the Architecture of a Multilayer Perceptron , 2009, Int. J. Pattern Recognit. Artif. Intell..

[11]  Q. M. Jonathan Wu,et al.  Low-resolution face recognition: a review , 2013, The Visual Computer.

[12]  Matti Pietikäinen,et al.  Local frequency descriptor for low-resolution face recognition , 2011, Face and Gesture 2011.

[13]  Sridha Sridharan,et al.  Evaluation of image resolution and super-resolution on face recognition performance , 2012, J. Vis. Commun. Image Represent..

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

[15]  Roman V. Yampolskiy,et al.  Avatar Face Recognition Using Wavelet Transform and Hierarchical Multi-scale LBP , 2011, 2011 10th International Conference on Machine Learning and Applications and Workshops.

[16]  Jie Chen,et al.  Fusing Local Patterns of Gabor Magnitude and Phase for Face Recognition , 2010, IEEE Transactions on Image Processing.

[17]  A. Suruliandi,et al.  Local binary patterns and its variants for face recognition , 2011, 2011 International Conference on Recent Trends in Information Technology (ICRTIT).

[18]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Pong C. Yuen,et al.  Very low resolution face recognition problem , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[20]  Jingjing Li,et al.  The combination of CSLBP and LBP feature for pedestrian detection , 2013, Proceedings of 2013 3rd International Conference on Computer Science and Network Technology.

[21]  Yong Man Ro,et al.  Using colour local binary pattern features for face recognition , 2010, 2010 IEEE International Conference on Image Processing.

[22]  Gonzalo Farias,et al.  Study of Local Matching-Based Facial Recognition Methods Using Thermal Infrared Imagery , 2015, Int. J. Pattern Recognit. Artif. Intell..

[23]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[24]  D. F. W. Yap,et al.  Medical image compression using block-based PCA algorithm , 2014, 2014 International Conference on Computer, Communications, and Control Technology (I4CT).

[25]  Thomas S. Huang,et al.  Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.