A study on using mid-wave infrared images for face recognition

The problem of face identication in the Mid-Wave InfraRed (MWIR) spectrum is studied in order to understand the performance of intra-spectral (MWIR to MWIR) and cross-spectral (visible to MWIR) matching. The contributions of this work are two-fold. First, a database of 50 subjects is assembled and used to illustrate the challenges associated with the problem. Second, a set of experiments is performed in order to demonstrate the possibility of MWIR intra-spectral and cross-spectral matching. Experiments show that images captured in the MWIR band can be eciently matched to MWIR images using existing techniques (originally not designed to address such a problem). These results are comparable to the baseline results, i.e., when comparing visible to visible face images. Experiments also show that cross-spectral matching (the heterogeneous problem, where gallery and probe sets have face images acquired in dierent spectral bands) is a very challenging problem. In order to perform cross-spectral matching, we use multiple texture descriptors and demonstrate that fusing these descriptors improves recognition performance. Experiments on a small database, suggests that the problem of cross-spectral matching requires further investigation.

[1]  Josef Kittler,et al.  Pattern recognition : a statistical approach , 1982 .

[2]  John Lester Miller,et al.  Principles Of Infrared Technology: A Practical Guide to the State of the Art , 1994 .

[3]  Arun Ross,et al.  Ascertaining Human Identity in Night Environments , 2011 .

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

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

[6]  Arun Ross,et al.  Feature level fusion of hand and face biometrics , 2005, SPIE Defense + Commercial Sensing.

[7]  Shigeyuki Tomita,et al.  Face identification using thermal image processing , 1997, Proceedings 6th IEEE International Workshop on Robot and Human Communication. RO-MAN'97 SENDAI.

[8]  Ioannis A. Kakadiaris,et al.  Pupil detection under lighting and pose variations in the visible and active infrared bands , 2011, 2011 IEEE International Workshop on Information Forensics and Security.

[9]  V. Štruc.  INface: A Toolbox for Illumination Invariant Face Recognition , 2009 .

[10]  Bir Bhanu,et al.  Distributed Video Sensor Networks , 2011 .

[11]  Jun Luo,et al.  Person-Specific SIFT Features for Face Recognition , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[12]  Lawrence B. Wolff,et al.  Illumination invariant face recognition using thermal infrared imagery , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[13]  Andrew Zisserman,et al.  Representing shape with a spatial pyramid kernel , 2007, CIVR '07.

[14]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[15]  Diego A. Socolinsky,et al.  Multispectral Face Recognition , 2008 .

[16]  Yaniv Taigman,et al.  Descriptor Based Methods in the Wild , 2008 .

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

[18]  Ali M. Reza,et al.  Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement , 2004, J. VLSI Signal Process..

[19]  Jian-Jun Zhang,et al.  Self quotient image for face recognition , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[20]  Matti Pietikäinen,et al.  Image Analysis with Local Binary Patterns , 2005, SCIA.

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

[22]  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).

[23]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[24]  A. Ross,et al.  Level Fusion Using Hand and Face Biometrics , 2005 .

[25]  Arun Ross,et al.  Handbook of Biometrics , 2007 .

[26]  Thirimachos Bourlai,et al.  Eye detection in the Middle-Wave Infrared spectrum: Towards recognition in the dark , 2011, 2011 IEEE International Workshop on Information Forensics and Security.

[27]  Anil K. Jain,et al.  Heterogeneous Face Recognition Using Kernel Prototype Similarities , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Andrea Salgian,et al.  Thermal face recognition in an operational scenario , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[29]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[30]  B. Krauskopf,et al.  Proc of SPIE , 2003 .

[31]  Andrea Salgian,et al.  Face recognition with visible and thermal infrared imagery , 2003, Comput. Vis. Image Underst..

[32]  Oliver Schreer,et al.  Real-time image processing and fusion for a new high-speed dual-band infrared camera , 2007, SPIE Defense + Commercial Sensing.

[33]  Bruce A. Draper,et al.  The CSU Face Identification Evaluation System: Its Purpose, Features, and Structure , 2003, ICVS.

[34]  Andrea Salgian,et al.  Face Recognition in the Dark , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.