Fusion of local and global features for face recognition

In recent years, there have been lots of research developments done in face recognition systems. Face recognition systems are widely used for access control, border control, surveillance and in law enforcement. Among other biometrics, it is the most natural and acceptable way of identifying an individual. Face recognition system does not require physical interaction with the user. Research is still being done intensively to produce systems that can cater for several challenges such as changes in pose, illumination, occlusion and low resolution images. Algorithms reported in literature use either global feature extraction or local feature extraction. In this work, a different technique is proposed that combines both global and local approaches for face recognition. The Principal Components Analysis (PCA) and Local Binary Patterns (LBP) have been employed. Face recognition yields a recognition rate of 90 % with PCA and 92 % with LBP. However, results show an improvement in recognition rate to 95 % when both approaches are fused.

[1]  Mandeep Kaur,et al.  Recognition of Facial Expressions with Principal Component Analysis and Singular Value Decomposition , 2010 .

[2]  Juyang Weng,et al.  Using Discriminant Eigenfeatures for Image Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Lawrence Sirovich,et al.  Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Anil K. Jain,et al.  Beyond fingerprinting. , 2008, Scientific American.

[5]  Michael I. Jordan,et al.  Advances in Neural Information Processing Systems 30 , 1995 .

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

[7]  Amir Moradi,et al.  Improving the energy efficiency of reversible logic circuits by the combined use of adiabatic styles , 2011, Integr..

[8]  Shang-Hong Lai,et al.  An Optical Flow-Based Approach to Robust Face Recognition Under Expression Variations , 2010, IEEE Transactions on Image Processing.

[9]  Arun Ross,et al.  Fingerprint matching using minutiae and texture features , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[10]  Jinguang Sun,et al.  U-face of applied research in the face recognition , 2010, 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE).

[11]  Markku Åberg,et al.  A Single Clocked Adiabatic Static Logic—A Proposal for Digital Low Power Applications , 2001, J. VLSI Signal Process..

[12]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[13]  Marian Stewart Bartlett,et al.  Face image analysis by unsupervised learning , 2001 .

[14]  Rui Zhao,et al.  Design of the modified energy recovery logic circuit , 2011, 2011 International Conference on Electric Information and Control Engineering.

[15]  H. Damasio,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence: Special Issue on Perceptual Organization in Computer Vision , 1998 .

[16]  Sabah Jassim,et al.  Image-Quality-Based Adaptive Face Recognition , 2010, IEEE Transactions on Instrumentation and Measurement.

[17]  H. Wechsler,et al.  Comparative Assessment of Independent Component Analysis (ICA) for Face Recognition , 1999 .

[18]  Peter A. Flach,et al.  Improving Accuracy and Cost of Two-class and Multi-class Probabilistic Classifiers Using ROC Curves , 2003, ICML.

[19]  Rama Chellappa,et al.  Face Recognition by Computers and Humans , 2010, Computer.

[20]  Atul Kumar Maurya,et al.  Adiabatic logic: Energy efficient technique for VLSI applications , 2011, 2011 2nd International Conference on Computer and Communication Technology (ICCCT-2011).

[21]  J. L. Wayman,et al.  Best practices in testing and reporting performance of biometric devices. , 2002 .

[22]  Bernhard Schölkopf,et al.  Kernel Principal Component Analysis , 1997, ICANN.

[23]  R. S. Jadon,et al.  COMPARISON BETWEEN FACE RECOGNITION ALGORITHM-EIGENFACES, FISHERFACES AND ELASTIC BUNCH GRAPH MATCHING , 2011 .

[24]  Michal Choras.  Ear Biometrics Based on Geometrical Feature Extraction , 2009, Progress in Computer Vision and Image Analysis.

[25]  Manish Kumar,et al.  Face Recognition Using Principle Component Analysis, Eigenface and Neural Network , 2010, 2010 International Conference on Signal Acquisition and Processing.

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

[27]  Vojin G. Oklobdzija,et al.  Clocked CMOS adiabatic logic with integrated single-phase power-clock supply , 2000, IEEE Trans. Very Large Scale Integr. Syst..

[28]  Rama Chellappa,et al.  Discriminant Analysis for Recognition of Human Face Images (Invited Paper) , 1997, AVBPA.

[29]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[31]  Luminita Vasiu,et al.  Biometric Recognition - Security and Privacy Concerns , 2004, ICETE.

[32]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[33]  L. Reyneri,et al.  Positive feedback in adiabatic logic , 1996 .

[34]  S. Samanta.  Adiabatic computing: A contemporary review , 2009, 2009 4th International Conference on Computers and Devices for Communication (CODEC).

[35]  Joshua B. Tenenbaum,et al.  Global Versus Local Methods in Nonlinear Dimensionality Reduction , 2002, NIPS.

[36]  Nestoras Tzartzanis,et al.  Low-power digital systems based on adiabatic-switching principles , 1994, IEEE Trans. Very Large Scale Integr. Syst..