- Research Article
- Open Access
Comparison of Spectral-Only and Spectral/Spatial Face Recognition for Personal Identity Verification
© Zhihong Pan et al. 2009
- Received: 29 September 2008
- Accepted: 8 April 2009
- Published: 26 May 2009
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.
- Face Recognition
- Recognition Rate
- Hyperspectral Image
- Iris Recognition
- Face Recognition System
Automatic personal identity authentication is an important problem in security and surveillance applications, where physical or logical access to locations, documents, and services must be restricted to authorized persons. Passwords or personal identification numbers (PINs) are often assigned to individuals for authentication. However, the password or PIN is vulnerable to unauthorized exploitation and can be forgotten. Biometrics, on the other hand, use personal intrinsic characteristics which are harder to compromise and more convenient to use. Consequently, the use of biometrics has been gaining acceptance for various applications. Many different sensing modalities have been developed to verify personal identities. Fingerprints are a widely used biometric. Iris recognition is an emerging technique for personal identification which is an active area of research. There are also studies to use voice and gait as primary or auxiliary means to verify personal identities.
Face recognition has been studied for many years for human identification and personal identity authentication and is increasingly used for its convenience and noncontact measurements. Most modern face recognition systems are based on the geometric characteristics of human faces in an image [1–4]. Accurate verification and identification performance has been demonstrated for these algorithms based on mug shot type photographic databases of thousands of human subjects under controlled environments [5, 6]. Various 3D face models [7, 8] and illumination models [9, 10] have been studied for pose and illumination-invariant face recognition. In addition to methods based on gray-scale and color face images over the visible spectrum, thermal infrared face images [11, 12] and hyperspectral face images  have also been used for face recognition experiments. An evaluation of different face recognition algorithms using a common dataset has been of general interest. This approach provides a solid basis to draw conclusions on the performance of different methods. The Face Recognition Technology (FERET) program  and the Face Recognition Vendor Test (FRVT)  are two programs which provided independent government evaluations for various face recognition algorithms and commercially available face recognition systems.
Most biometric methods, including face recognition methods, are subject to possible false acceptance or rejection. Although biometric information is difficult to duplicate, these methods are not immune to forgery, or so-called spoofing. This is a concern for automatic personal identity authentication since intruders can use artificial materials or objects to gain unauthorized access. There are reports showing that fingerprint sensor devices have been deceived by Gummi fingers in Japan  and fake latex fingerprints in Germany . Face and iris recognition systems can also be compromised since they use external observables . To counter this vulnerability, many biometric systems employ a liveness detection function to foil attempts at biometric forgery [17, 18]. To improve system accuracy, there is strong interest in research to combine multiple biometric characteristics for multimodal personal identity authentication [19, 20]. Since hyperspectral sensors capture spectral and spatial information they provide the potential for improved personal identity verification.
Methods that have been developed consider the use of representations for visible wavelength color images for face recognition [21, 22] as well as the combination of color and 3D information . In this work, we examine the use of combined spectral/spatial information for face recognition over the near-infrared (NIR) spectral range. We show that the use of spatial information can be used to improve on the performance of spectral-only methods . We also use a large NIR hyperspectral dataset to show that the choice of spectral band over the NIR does not have a significant effect on the performance of single-band eigenface methods. On the other hand, we show that band selection does have a significant effect on the performance of multiband methods. In this paper we develop a new representation called the spectral-face which preserves both high-spectral and high-spatial resolution. We show that the spectral eigenface representation outperforms single-band eigenface methods and has performance that is comparable to multiband eigenface methods but at a lower computational cost.
Given the th band of hyperspectral images and , the Mahalanobis Cosine distance  is used to measure the similarity of the two images. Let be the projection of the th band of onto the th eigenface and let be the standard deviation of the projections from all of the th band images onto the th eigenface. The Mahalanobis projection of is where . Let be the similarly computed Mahalanobis projection of . The Mahalanobis Cosine distance between and for the th band is defined by
We have shown that both spatial and spectral features in hyperspectral face images provide useful discriminants for recognition. Thus, we can consider the extent of performance improvements when both features are utilized. We define a distance between images U and V using
Multimodal personal identity authentication systems have gained popularity. Hyperspectral imaging systems capture both spectral and spatial information. The previous work  has shown that spectral signatures are powerful discriminants for face recognition in hyperspectral images. In this work, various methods that utilize spectral and/or spatial features were evaluated using a hyperspectral face image dataset. The single-band eigenface method uses spatial features exclusively and performed better than the pure spectral method. However, the computational requirements increase significantly for eigenface generation and projection. The recognition rate was further improved by using multiband eigenface methods which require more computation. The best performance was achieved with the highest computational complexity by using principal component bands. The spectral eigenface method transforms a multiband hyperspectral image to a spectral-face image which samples from all of the bands while preserving spatial resolution. We showed that this method performs as well as the PCT-based multiband method but with a much lower computational requirement.
This work was conducted when the author was with the Computer Vision Laboratory at the University of California, Irvine, USA. This work has been supported by the DARPA Human Identification at a Distance Program through AFOSR Grant F49620-01-1-0058. This work has also been supported by the Laser Microbeam and Medical Program (LAMMP) and NIH Grant RR01192. The data was acquired at the Beckman Laser Institute on the UC Irvine campus. The authors would like to thank J. Stuart Nelson and Montana Compton for their valuable assistance in the process of IRB approval and human subject recruitment.
- Chellappa R, Wilson CL, Sirohey S: Human and machine recognition of faces: a survey. Proceedings of the IEEE 1995,83(5):705-740. 10.1109/5.381842View ArticleGoogle Scholar
- Etemad K, Chellappa R: Discriminant analysis for recognition of human face images. Journal of the Optical Society of America A 1997,14(8):1724-1733. 10.1364/JOSAA.14.001724View ArticleGoogle Scholar
- Moghaddam B, Pentland A: Probabilistic visual learning for object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997,19(7):696-710. 10.1109/34.598227View ArticleGoogle Scholar
- Wiskott L, Fellous J-M, Krüger N, von der Malsburg C: Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997,19(7):775-779. 10.1109/34.598235View ArticleGoogle Scholar
- Phillips PJ, Moon H, Rizvi SA, Rauss PJ: The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000,22(10):1090-1104. 10.1109/34.879790View ArticleGoogle Scholar
- Phillips PJ, Grother P, Micheals R, Blackburn DM, Tabassi E, Bone M: Face recognition vendor test 2002: overview and summary. Defense Advanced Re-search Projects Agency, Arlington, Va, USA; March 2003.View ArticleGoogle Scholar
- Blanz V, Vetter T: Face recognition based on fitting a 3D morphable model. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003,25(9):1063-1074. 10.1109/TPAMI.2003.1227983View ArticleGoogle Scholar
- Chang KI, Bowyer KW, Flynn PJ: An evaluation of multimodal 2D+3D face biometrics. IEEE Transactions on Pattern Analysis and Machine Intelligence 2005,27(4):619-624.View ArticleGoogle Scholar
- Adini Y, Moses Y, Ullman S: Face recognition: the problem of compensating for changes in illumination direction. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997,19(7):721-732. 10.1109/34.598229View ArticleGoogle Scholar
- Lee K-C, Ho J, Kriegman DJ: Acquiring linear subspaces for face recognition under variable lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence 2005,27(5):684-698.View ArticleGoogle Scholar
- Socolinsky DA, Selinger A, Neuheisel JD: Face recognition with visible and thermal infrared imagery. Computer Vision and Image Understanding 2003,91(1-2):72-114. 10.1016/S1077-3142(03)00075-4View ArticleGoogle Scholar
- Wilder J, Phillips PJ, Jiang C, Wiener S: Comparison of visible and infra-red imagery for face recognition. Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (AFGR '96), October 1996, Killington, Vt, USA 182-187.View ArticleGoogle Scholar
- Pan Z, Healey G, Prasad M, Tromberg B: Face recognition in hyperspectral images. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003,25(12):1552-1560. 10.1109/TPAMI.2003.1251148View ArticleGoogle Scholar
- Leyden J: Gummi bears defeat fingerprint sensors. The Register 2002.Google Scholar
- Harrison A: Hackers claim new fingerprint biometric attack. Security Focus 2003.Google Scholar
- Lewis M, Statham P: CESG biometric security capabilities programme: method, results and research challenges. Biometric Consortium Conference, September 2004, Crystal City, Va, USAGoogle Scholar
- Bigun J, Fronthaler H, Kollreider K: Assuring liveness in biometric identity authentication by real-time face tracking. Proceedings of IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety (CIHSPS '04), July 2004, Venice, Italy 104-111.Google Scholar
- Tan T, Ma L: Iris recognition: recent progress and remaining challenges. Biometric Technology for Human Identification, April 2004, Orlando, Fla, USA, Proceedings of SPIE 5404: 183-194.View ArticleGoogle Scholar
- Kittler J, Matas J, Jonsson K, Ramos Sánchez MU: Combining evidence in personal identity verification systems. Pattern Recognition Letters 1997,18(9):845-852. 10.1016/S0167-8655(97)00062-7View ArticleGoogle Scholar
- Kittler J, Messer K: Fusion of multiple experts in multimodal biometric personal identity verification systems. Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, December 2002, Kauai, Hawaii, USA 3-12.View ArticleGoogle Scholar
- Yang J, Zhang D, Xu Y, Yang J-Y: Recognize color face images using complex eigenfaces. Proceedings of International Conference on Advances in Biometrics (ICB '06), January 2006, Hong Kong, Lecture Notes in Computer Science 3832: 64-68.View ArticleGoogle Scholar
- Yoo S, Park R-H, Sim D-G: Investigation of color spaces for face recognition. Proceedings of IAPR Conference on Machine Vision Applications (MVA '07), May 2007, Tokyo, Japan 106-109.Google Scholar
- Tsalakanidou F, Tzovaras D, Strintzis MG: Use of depth and colour eigenfaces for face recognition. Pattern Recognition Letters 2003,24(9-10):1427-1435. 10.1016/S0167-8655(02)00383-5MATHView ArticleGoogle Scholar
- Pan Z, Healey G, Prasad M, Tromberg B: Face recognition in hyperspectral images. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03), June 2003, Madison, Wis, USA. Volume 1. Institute of Electrical and Electronics Engineers; 334-339.Google Scholar
- Bolme D, Beveridge JR, Teixeira M, Draper BA: The CSU face identification evaluation system: its purpose, features and structure. Proceedings of the 3rd International Conference Computer Vision Systems (ICVS '03), April 2003, Graz, Austria, Lecture Notes in Computer Science 2626: 304-313.Google Scholar
- Turk MA, Pentland AP: Face recogntion using eigenfaces. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '91), June 1991, Maui, Hawaii, USA 586-591.Google Scholar
- Beveridge JR, Bolme DS, Teixeira M, Draper B: The CSU face identification evaluation system user's guide: version 5.0. Computer Science Department, Colorado State University, Fort Collins, Colo, USA; May 2003.Google Scholar
- Papoulis A: Probability and Statistics. Prentice-Hall, Englewood Cliffs, NJ, USA; 1990.MATHGoogle Scholar
- Ready PJ, Wintz PA: Information extraction, SNR improvement, and data compression in multispectral imagery. IEEE Transactions on Communications 1973,21(10):1123-1131. 10.1109/TCOM.1973.1091550View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.