Contourlet Based Image Compression for Wireless Communication in Face Recognition System

This paper proposes to use Contourlet transform for image compression and feature extraction for wireless face recognition system. The properties of face images and face recognition techniques are incorporated into the design of wireless transmission for such a system. The reasons for utilizing contourlet transform are two-folded. Firstly, in face recognition, the edge information is crucial in deriving features, and the edges within a face image are not just horizontal or vertical. When the coefficients are transmitted through the fading channel, the reconstruction from the Stein-thresholded noisy coefficients by contourlet achieves less mean square error than by wavelet. Secondly, when the network resources limit the transmission of full-set coefficients, the lower band coefficients can serve as a scaled-down version of the face image, for a coarser face recognition as screening. A prioritized transmission of the coefficients take full advantage of the wireless channel. Simulation shows that the wireless face recognition system works as well as a wired one, while gaining the cost efficiency, and the flexibility in deployment. An interesting phenomenon is discovered on FERET database that when the transmission error rate is increased linearly, the recognition performance degradation is not linear; instead, the performance stays the same for a large range of error rates, which illustrates that contourlet based face recognition system can tolerate the transmission error up to some threshold.

[1]  Mislav Grgic,et al.  Image Compression Effects in Face Recognition Systems , 2007 .

[2]  Hua Yu,et al.  A direct LDA algorithm for high-dimensional data - with application to face recognition , 2001, Pattern Recognit..

[3]  Lisa Ann Osadciw,et al.  Detecting Sybil attacks in image senor network using cognitive intelligence , 2007, SANET '07.

[4]  M. Do.  Directional multiresolution image representations , 2002 .

[5]  Huiqin Wang,et al.  Face recognition based on HMM in compressed domain , 2006, Electronic Imaging.

[6]  Ja-Chen Lin,et al.  A new LDA-based face recognition system which can solve the small sample size problem , 1998, Pattern Recognit..

[7]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Ravi Kothari,et al.  Fractional-Step Dimensionality Reduction , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

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

[10]  R. Eslami,et al.  The contourlet transform for image denoising using cycle spinning , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[11]  M. Faundez-Zanuy.  Face recognition in a transformed domain , 2003, IEEE 37th Annual 2003 International Carnahan Conference onSecurity Technology, 2003. Proceedings..

[12]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[13]  Lisa Ann Osadciw,et al.  Contourlet Based Image Recovery and De-noising Through Wireless Fading Channels , 2005 .

[14]  Lisa Ann Osadciw,et al.  Prediction of Sybil attack on WSN using Bayesian network and swarm intelligence , 2008, SPIE Defense + Commercial Sensing.

[15]  U. Desai,et al.  A transform domain face recognition approach , 1999, Proceedings of IEEE. IEEE Region 10 Conference. TENCON 99. 'Multimedia Technology for Asia-Pacific Information Infrastructure' (Cat. No.99CH37030).

[16]  Gerhard Rigoll,et al.  High quality face recognition in JPEG compressed images , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[17]  Konstantinos N. Plataniotis,et al.  Face recognition using LDA-based algorithms , 2003, IEEE Trans. Neural Networks.

[18]  M.A. Ferrer,et al.  Facial identification using transformed domain by SVM , 2004, 38th Annual 2004 International Carnahan Conference on Security Technology, 2004..

[19]  Lisa Ann Osadciw,et al.  Fusion for Component Based Face Recognition , 2007, 2007 41st Annual Conference on Information Sciences and Systems.

[20]  M. Grgic,et al.  Towards Face Recognition in JPEG2000 Compressed Domain , 2007, 2007 14th International Workshop on Systems, Signals and Image Processing and 6th EURASIP Conference focused on Speech and Image Processing, Multimedia Communications and Services.

[21]  Emmanuel J. Candès,et al.  The curvelet transform for image denoising , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[22]  Wei Huang,et al.  Face Recognition Based on Curvefaces , 2007, Third International Conference on Natural Computation (ICNC 2007).

[23]  Minh N. Do,et al.  Ieee Transactions on Image Processing the Contourlet Transform: an Efficient Directional Multiresolution Image Representation , 2022 .

[24]  Bailing Zhang,et al.  Face Recognition by Combining Kernel Associative Memory and Gabor Transforms , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[25]  Farzin Mokhtarian,et al.  Efficient face recognition for wireless surveillance systems , 2007 .

[26]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[27]  Andrew Teoh Beng Jin,et al.  An efficient method for human face recognition using wavelet transform and Zernike moments , 2004 .