One sample per person face recognition based on particle swarm optimisation

One sample per person face recognition problem is a challenging problem in face recognition. The authors propose a basis plus variety model on a high-dimensional unit sphere to tackle the problem. In the model, the query image is approximately a linear combination of the basis image and variety image. The basis images are the neutral images of subjects and variety images are generalised from multi-sample subjects in the gallery. Particle swarm optimisation is chosen to find out the optimum combination of the basis and variety in terms of the minimum L2 distance relative to the query image. The identity of the query image is equal to the identity of the basis image of the optimum combination. Experiments on the extended Yale face database B and AR database are provided to show the validity of the proposed algorithm.

[1]  Yu-Chiang Frank Wang,et al.  With one look: robust face recognition using single sample per person , 2013, ACM Multimedia.

[2]  Jun Guo,et al.  Extended SRC: Undersampled Face Recognition via Intraclass Variant Dictionary , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Jun Guo,et al.  Equidistant prototypes embedding for single sample based face recognition with generic learning and incremental learning , 2014, Pattern Recognit..

[4]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[6]  Chu-Song Chen,et al.  Intrinsic Illumination Subspace for Lighting Insensitive Face Recognition , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[7]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Zhi-Hua Zhou,et al.  Face recognition from a single image per person: A survey , 2006, Pattern Recognit..

[9]  Ioan Cristian Trelea,et al.  The particle swarm optimization algorithm: convergence analysis and parameter selection , 2003, Inf. Process. Lett..

[10]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[11]  Allen Y. Yang,et al.  Single-Sample Face Recognition with Image Corruption and Misalignment via Sparse Illumination Transfer , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[13]  Mohammed Bennamoun,et al.  Linear Regression for Face Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[15]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

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

[17]  Jun Guo,et al.  In Defense of Sparsity Based Face Recognition , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Guodong Guo,et al.  Face recognition by support vector machines , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

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