Are face recognition methods useful for classifying ships?

Face recognition research has gained significant interest in recent years which has resulted in the development of many state-of-the-art methods. However, it is not well-known how domain specific these methods are to the problem of face recognition. Could these algorithms be used to classify and identify other objects, such as ships seen from electro-optical satellite imagery? Face recognition research shares many of the same challenges with many other types of classification research, such as illumination, pose and resolution variation. Therefore, a study of this type is warranted. We present a comparison of several classical (e.g. eigenfaces and fisherfaces) as well as recent state-of-the-art face recognition methods (e.g. sparse representation and local binary patterns) using one standard face database and two databases of ship images collected from satellite imagery. An analysis of these results as well as future directions conclude the paper.

[1]  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..

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

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

[4]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[5]  Marian Stewart Bartlett,et al.  Face recognition by independent component analysis , 2002, IEEE Trans. Neural Networks.

[6]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[7]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[8]  Zhenhua Guo,et al.  Hierarchical multiscale LBP for face and palmprint recognition , 2010, 2010 IEEE International Conference on Image Processing.

[9]  Haiping Lu,et al.  MPCA: Multilinear Principal Component Analysis of Tensor Objects , 2008, IEEE Transactions on Neural Networks.

[10]  Patrick J. Flynn,et al.  Preliminary Face Recognition Grand Challenge Results , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[11]  Shengcai Liao,et al.  Learning Multi-scale Block Local Binary Patterns for Face Recognition , 2007, ICB.

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

[13]  David G. Stork,et al.  Pattern Classification , 1973 .

[14]  Antonio Albiol,et al.  Face recognition using HOG-EBGM , 2008, Pattern Recognit. Lett..

[15]  Andrew Zisserman,et al.  "Who are you?" - Learning person specific classifiers from video , 2009, CVPR.

[16]  Lijun Yin,et al.  Multi-view facial expression recognition , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[17]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[19]  Xihong Wu,et al.  Boosting Local Binary Pattern (LBP)-Based Face Recognition , 2004, SINOBIOMETRICS.

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

[21]  Shigeo Abe DrEng Pattern Classification , 2001, Springer London.