The Effect of Image Compression on Face Recognition Algorithms

Abstract: Face recognition becomes an important field via the revolution in technology and computer vision. This paper concentrated on recognition rate of face recognition algorithms. The There are many researches related to face recognition algorithms examined are: Principal Component Analysis, Two Dimensional Principal Component Analysis in Column Direction, Two Dimensional Principal Component Analysis in Row Direction and Two Dimensional Two Directional Principal Component Analysis. All these algorithms are implemented into two environments: training environment and recognition environment. Then a comparison between these four algorithms with respect to recognition rate is implemented. The proposed algorithm is implemented via Discrete Wavelet Transform (DWT) that minimizes the images size. A complexity reduction is achieved by optimizing the number of operations needed. This optimization does not increase the recognition rate only, but also reduce the execution time. A recognition rate improvement of 4% to 5% is achieved by introducing DWT through PCA algorithms.

[1]  Chi Fang,et al.  2D face fitting-assisted 3D face reconstruction for pose-robust face recognition , 2011, Soft Comput..

[2]  Zhan Yu,et al.  Evolutionary fusion of a multi-classifier system for efficient face recognition , 2009 .

[3]  Rin-ichiro Taniguchi,et al.  Feature map sharing hypercolumn model for shift invariant face recognition , 2009, Artificial Life and Robotics.

[4]  Reza Ebrahimpour,et al.  Improving mixture of experts for view-independent face recognition using teacher-directed learning , 2011, Machine Vision and Applications.

[5]  Majid Nili Ahmadabadi,et al.  Attention control with reinforcement learning for face recognition under partial occlusion , 2011, Machine Vision and Applications.

[6]  Yu Qiao,et al.  Face recognition based on gradient gabor feature and Efficient Kernel Fisher analysis , 2010, Neural Computing and Applications.

[7]  Asit K. Datta,et al.  Illumination and noise tolerant face recognition based on eigen-phase correlation filter modified by Mexican hat wavelet , 2009 .

[8]  Zhang Yi,et al.  A New Incremental PCA Algorithm With Application to Visual Learning and Recognition , 2009, Neural Processing Letters.

[9]  Mislav Grgic,et al.  SCface – surveillance cameras face database , 2011, Multimedia Tools and Applications.

[10]  Haixian Wang,et al.  Structural two-dimensional principal component analysis for image recognition , 2011, Machine Vision and Applications.

[11]  M. E. Sokolov,et al.  Face recognition using “lateral inhibition” function features , 2009, Optical Memory and Neural Networks.

[12]  Wu-Sheng Lu,et al.  Perfect histogram matching PCA for face recognition , 2010, Multidimens. Syst. Signal Process..

[13]  Mahantapas Kundu,et al.  High-speed face recognition using self-adaptive radial basis function neural networks , 2009, Neural Computing and Applications.

[14]  Khalid Chougdali,et al.  Kernel relevance weighted discriminant analysis for face recognition , 2009, Pattern Analysis and Applications.

[15]  Josef Kittler,et al.  On design and optimization of face verification systems that are smart-card based , 2010, Machine Vision and Applications.

[16]  Debesh Choudhury,et al.  Three-dimensional human face recognition , 2009 .

[17]  Guoxing Jiang,et al.  An LBP-based multi-scale illumination preprocessing method for face recognition , 2009 .

[18]  C. S. Rai,et al.  Feature selection for face recognition: a memetic algorithmic approach , 2009 .