A Hybrid Local Feature for Face Recognition

Efficient face encoding is an important issue in the area of face recognition. Compared to holistic features, local features have received increasing attention due to their good robustness to pose and illumination changes. In this paper, based on the histogram-based interest points and the speeded up robust features, we propose a hybrid local face feature, which provides a proper balance between the computational speed and discriminative power. Experiments on three databases demonstrate the effectiveness of the proposed method as well as its robustness to the main challenges of face recognition and even in practical environment.

[1]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[2]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[3]  Kuldip K. Paliwal,et al.  Polynomial features for robust face authentication , 2002, Proceedings. International Conference on Image Processing.

[4]  P. Peer,et al.  Illumination independent color-based face detection , 2003, 3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the.

[5]  Zheng-Jun Zha,et al.  Evaluation of histogram based interest point detector in web image classification and search , 2010, 2010 IEEE International Conference on Multimedia and Expo.

[6]  Anil K. Jain,et al.  Handbook of Face Recognition, 2nd Edition , 2011 .

[7]  Koen E. A. van de Sande,et al.  Evaluating Color Descriptors for Object and Scene Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[9]  Luc Van Gool,et al.  Modeling scenes with local descriptors and latent aspects , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[10]  Andrew Zisserman,et al.  An Affine Invariant Salient Region Detector , 2004, ECCV.

[11]  Hwann-Tzong Chen,et al.  Histogram-based interest point detectors , 2009, CVPR.

[12]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[13]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[14]  Konstantinos N. Plataniotis,et al.  Regularization studies on LDA for face recognition , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[15]  Jiří Matas,et al.  Computer Vision - ECCV 2004 , 2004, Lecture Notes in Computer Science.

[16]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[17]  Shuicheng Yan,et al.  Exploring Feature Descritors for Face Recognition , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[18]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[19]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

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

[21]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[22]  Jasna Maver,et al.  Self-Similarity and Points of Interest , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Tsuhan Chen,et al.  Eigenspace updating for non-stationary process and its application to face recognition , 2003, Pattern Recognit..