Comparison of Spectral-Only and Spectral/Spatial Face Recognition for Personal Identity Verification

Face recognition based on spatial features has been widely used for personal identity verification for security-related applications. Recently, near-infrared spectral reflectance properties of local facial regions have been shown to be sufficient discriminants for accurate face recognition. In this paper, we compare the performance of the spectral method with face recognition using the eigenface method on single-band images extracted from the same hyperspectral image set. We also consider methods that use multiple original and PCA-transformed bands. Lastly, an innovative spectral eigenface method which uses both spatial and spectral features is proposed to improve the quality of the spectral features and to reduce the expense of the computation. The algorithms are compared using a consistent framework.

[1]  B. Draper,et al.  The CSU Face Identification Evaluation System User ’ s Guide : Version 4 . 0 , 2002 .

[2]  Bruce J. Tromberg,et al.  Face Recognition in Hyperspectral Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  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.

[4]  Patrick J. Flynn,et al.  An evaluation of multimodal 2D+3D face biometrics , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Josef Kittler,et al.  Fusion of multiple experts in multimodal biometric personal identity verification systems , 2002, Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing.

[6]  Rama Chellappa,et al.  Human and machine recognition of faces: a survey , 1995, Proc. IEEE.

[7]  Jiri Matas,et al.  Combining evidence in personal identity verification systems , 1997, Pattern Recognit. Lett..

[8]  P. Wintz,et al.  Information Extraction, SNR Improvement, and Data Compression in Multispectral Imagery , 1973, IEEE Trans. Commun..

[9]  Alex Pentland,et al.  Probabilistic Visual Learning for Object Representation , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Norbert Krüger,et al.  Face recognition by elastic bunch graph matching , 1997, Proceedings of International Conference on Image Processing.

[11]  P. Jonathon Phillips,et al.  Face recognition vendor test 2002 , 2003, 2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443).

[12]  P. Jonathon Phillips,et al.  Face Recognition Vendor Test 2002: Overview and Summary , 2003 .

[13]  David J. Kriegman,et al.  Acquiring linear subspaces for face recognition under variable lighting , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Dong-Gyu Sim,et al.  Investigation of Color Spaces for Face Recognition , 2007, MVA.

[15]  Thomas Vetter,et al.  Face Recognition Based on Fitting a 3D Morphable Model , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

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

[17]  Tieniu Tan,et al.  Recognize Color Face Images Using Complex Eigenfaces , 2006 .

[18]  A. Papoulis.  Probability and Statistics , 1989 .

[19]  Joseph Wilder,et al.  Comparison of visible and infra-red imagery for face recognition , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

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

[21]  J. Bigun,et al.  Assuring liveness in biometric identity authentication by real-time face tracking , 2004, Proceedings of the 2004 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety, 2004. CIHSPS 2004..

[22]  Michael G. Strintzis,et al.  Use of depth and colour eigenfaces for face recognition , 2003, Pattern Recognit. Lett..

[23]  Tieniu Tan,et al.  Iris recognition: recent progress and remaining challenges , 2004, SPIE Defense + Commercial Sensing.

[24]  Shimon Ullman,et al.  Face Recognition: The Problem of Compensating for Changes in Illumination Direction , 1997, IEEE Trans. Pattern Anal. Mach. Intell..