Random Features Applied to Face Recognition

In this paper we show how a simplified version of a describing vector can be used to efficiently recognize complex objects. We describe how simplified vectors are randomly obtained from complete describing vectors and how these simplified versions can be used to recognize faces. We compare the efficiency of the proposal against PCA using several known distance classifiers with a benchmark of faces.

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

[2]  Tom M. Mitchell,et al.  Learning to Decode Cognitive States from Brain Images , 2004, Machine Learning.

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

[4]  I. Jolliffe.  Principal Component Analysis , 2002 .

[5]  Karl J. Friston,et al.  Unified SPM–ICA for fMRI analysis , 2005, NeuroImage.

[6]  H. Abdi,et al.  Principal Component and Neural Network Analyses of Face Images: What Can Be Generalized in Gender Classification? , 1997, Journal of mathematical psychology.

[7]  Aapo Hyvärinen,et al.  Survey on Independent Component Analysis , 1999 .

[8]  Vytautas Perlibakas,et al.  Distance measures for PCA-based face recognition , 2004, Pattern Recognit. Lett..

[9]  Paul B. Kantor,et al.  IR principles for content-based indexing and retrieval of functional brain images , 2006, CIKM '06.

[10]  Stephen Smith,et al.  FSL: New tools for functional and structural brain image analysis , 2001, NeuroImage.

[11]  Stephen C. Strother,et al.  Support vector machines for temporal classification of block design fMRI data , 2005, NeuroImage.

[12]  Allen R. Hanson,et al.  Maximum-weight bipartite matching technique and its application in image feature matching , 1996, Other Conferences.

[13]  Michael Brady,et al.  Feature-based correspondence: an eigenvector approach , 1992, Image Vis. Comput..

[14]  Karl J. Friston,et al.  Analysis of functional MRI time‐series , 1994, Human Brain Mapping.

[15]  T. Kohonen,et al.  Self-organizing semantic maps , 1989, Biological Cybernetics.

[16]  George Bebis,et al.  Face recognition experiments with random projection , 2005, SPIE Defense + Commercial Sensing.

[17]  Jonathan D. Cohen,et al.  An fMRI Investigation of Emotional Engagement in Moral Judgment , 2001, Science.

[18]  T. Sejnowski,et al.  Human Brain Mapping 6:368–372(1998) � Independent Component Analysis of fMRI Data: Examining the Assumptions , 2022 .

[19]  Helen E. Fisher,et al.  Reward, motivation, and emotion systems associated with early-stage intense romantic love. , 2005, Journal of neurophysiology.

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

[21]  Samuel Kaski,et al.  Self organization of a massive document collection , 2000, IEEE Trans. Neural Networks Learn. Syst..

[22]  Vasileios Megalooikonomou,et al.  AN EFFECTIVE AND EFFICIENT TECHNIQUE FOR SEARCHING FOR SIMILAR BRAIN ACTIVATION PATTERNS , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[23]  Heikki Mannila,et al.  Random projection in dimensionality reduction: applications to image and text data , 2001, KDD '01.

[24]  Samuel Kaski,et al.  Dimensionality reduction by random mapping: fast similarity computation for clustering , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[25]  Stephen M. Smith,et al.  Probabilistic independent component analysis for functional magnetic resonance imaging , 2004, IEEE Transactions on Medical Imaging.