Low-memory requirement and efficient face recognition system based on DCT pyramid

Face recognition (FR) is a challenging issue due to variations in pose, illumination, and expression. In this paper, a face recognition system with low-memory requirement and accurate recognition is presented. It is based on extraction of features with the DCT pyramid, in contrast to the conventional method of wavelet decomposition. The DCT pyramid performed on each face image decomposes it into an approximation subband and the reversed L-shape blocks containing the high frequency coefficients of the DCT pyramid. A set of simple block-based statistical measures is calculated from the extracted DCT pyramid subbands. This set of statistical measures is an efficient way of reducing the dimensionality of the feature vectors. Experimental results on the standard ORL and FERET databases show that the proposed method achieves more accurate face recognition than the wavelet-based methods. Moreover, it outperforms the other well known methods such as PCA and the block-based DCT with the zigzag scanning in terms of accuracy and memory requirement.

[1]  Pong C. Yuen,et al.  Human face recognition using PCA on wavelet subband , 2000, J. Electronic Imaging.

[2]  LinLin Shen,et al.  A review on Gabor wavelets for face recognition , 2006, Pattern Analysis and Applications.

[3]  Mohammed Ghanbari,et al.  Layered image coding using the DCT pyramid , 1995, IEEE Trans. Image Process..

[4]  Baozong Yuan,et al.  A novel approach for human face detection from color images under complex background , 2001, Pattern Recognit..

[5]  Jen-Tzung Chien,et al.  Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Martin D. Levine,et al.  Face Recognition Using the Discrete Cosine Transform , 2001, International Journal of Computer Vision.

[8]  Daphne Koller,et al.  Toward Optimal Feature Selection , 1996, ICML.

[9]  Sung-Il Chien,et al.  Skin Color Detection through Estimation and Conversion of Illuminant Color Under Various Illuminations , 2007, IEEE Transactions on Consumer Electronics.

[10]  Yuxiao Hu,et al.  Face recognition using Laplacianfaces , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Ioannis Pitas,et al.  A novel method for automatic face segmentation, facial feature extraction and tracking , 1998, Signal Process. Image Commun..

[12]  Fei Zuo,et al.  Real-time embedded face recognition for smart home , 2001 .

[13]  Mohammed Ghanbari,et al.  An efficient layered video codec based on DCT pyramid , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[14]  Dong-Sun Kim,et al.  Embedded face recognition based on fast genetic algorithm for intelligent digital photography , 2006, IEEE Transactions on Consumer Electronics.

[15]  Chengjun Liu,et al.  Evolutionary Pursuit and Its Application to Face Recognition , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Driss Aboutajdine,et al.  Novel face recognition approach based on steerable pyramid feature extraction , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[17]  Bülent Sankur,et al.  ARTICLE IN PRESS Image and Vision Computing xx (2005) 1–9 www.elsevier.com/locate/imavis , 2004 .

[18]  Georgios Tziritas,et al.  Wavelet packet analysis for face recognition , 2000, Image Vis. Comput..

[19]  Zhiwei Zhu,et al.  Robust real-time eye detection and tracking under variable lighting conditions and various face orientations , 2005, Comput. Vis. Image Underst..

[20]  Hyeran Byun,et al.  A new face authentication system for memory-constrained devices , 2003, IEEE Trans. Consumer Electron..

[21]  Anil K. Jain,et al.  Dimensionality reduction using genetic algorithms , 2000, IEEE Trans. Evol. Comput..