Thermal infrared (IR) images focus on changes of temperature distribution on facial muscles and blood vessels. These temperature changes can be regarded as texture features of images. A comparative study of face two recognition methods working in thermal spectrum is carried out in this paper. In the first approach, the training images and the test images are processed with Haar wavelet transform and the LL band and the average of LH/HL/HH bands subimages are created for each face image. Then a total confidence matrix is formed for each face image by taking a weighted sum of the corresponding pixel values of the LL band and average band. For LBP feature extraction, each of the face images in training and test datasets is divided into 161 numbers of subimages, each of size 8 × 8 pixels. For each such subimages, LBP features are extracted which are concatenated in manner. PCA is performed separately on the individual feature set for dimensionality reduction. Finally, two different classifiers namely multilayer feed forward neural network and minimum distance classifier are used to classify face images. The experiments have been performed on the database created at our own laboratory and Terravic Facial IR Database. 1. Introduction In the modern society, there is an increasing need to track and recognize persons automatically in various areas such as in the areas of surveillance, closed circuit television (CCTV) control, user authentication, human computer interface (HCI), daily attendance register, airport security checks, and immigration checks [1–3]. Such requirement for reliable personal identification in computerized access control has resulted in an increased interest in biometrics. The key element of biometric technology is its ability to identify a human being and enforce security. Nearly all-biometric systems work in the same manner. First, a person is registered into a database using a specified method. Information about a certain characteristic of the human is captured. This information is usually placed through an algorithm that turns the information into a code that the database stores. When the person needs to be identified, the system will take the information about the person again, translate this new information with the algorithm, and then compare the new code with the stored ones in the database to find out a possible match. Biometrics use physical characteristics or personal traits to identify a person. Physical feature is suitable for identity purpose and generally obtained from living human body. Commonly used physical features are
References
[1]
W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face recognition: a literature survey,” ACM Computing Surveys, vol. 35, no. 4, pp. 399–458, 2003.
[2]
P. J. Phillips, A. Martin, C. L. Wilson, and M. Przybocki, “Introduction to evaluating biometric systems,” Computer, vol. 33, no. 2, pp. 56–63, 2000.
[3]
A. Samal and P. A. Iyengar, “Automatic recognition and analysis of human faces and facial expressions: a survey,” Pattern Recognition, vol. 25, no. 1, pp. 65–77, 1992.
[4]
A. Gupta and S. K. Majumdar, “Machine Recognition of Human Face,” http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.127.4323&rep=rep1&type=pdf.
[5]
D. A. Socolinsky and A. Selinger, “A comparative analysis of face recognition performance with visible and thermal infrared imagery,” in Proceedings of the International Conference on Pattern Recognition, vol. 4, pp. 217–222, Quebec, Canada, 2002.
[6]
D. A. Socolinsky, A. Selinger, and J. D. Neuheisel, “Face recognition with visible and thermal infrared imagery,” Computer Vision and Image Understanding, vol. 91, no. 1-2, pp. 72–114, 2003.
[7]
S. Wu, Z.-J. Fang, Z.-H. Xie, and W. Liang, “Blood Perfusion Models for Infrared Face Recognition,” School of information technology, Jiangxi University of Finance and Economics, China.
[8]
S. G. Kong, J. Heo, B. R. Abidi, J. Paik, and M. A. Abidi, “Recent advances in visual and infrared face recognition—a review,” Computer Vision and Image Understanding, vol. 97, no. 1, pp. 103–135, 2005.
[9]
P. Buddharaju, I. T. Pavlidis, and P. Tsiamyrtzis, “Pose-invariant physiological face recognition in the thermal infrared spectrum,” in Proceedings of the Conference on Computer Vision and Pattern Recognition Workshops, p. 133, Washington, DC, USA, June 2006.
[10]
C. Manohar, Extraction of superficial vasculature in thermal imaging [M.S. thesis], Department of Electrical Engineering, University of Houston, Houston, Tex, USA, 2004.
[11]
A. C. Guyton and J. E. Hall, Textbook of Medical Physiology, W.B. Saunders Company, Philadelphia, Pa, USA, 9th edition, 1996.
[12]
F. Prokoski, “History, current status, and future of infrared identification,” in Proceedings of the IEEE Workshop Computer Vision Beyond Visible Spectrum: Methods and Applications, pp. 5–14, 2000.
[13]
S. Bryan Morse, Lecture 2: Image Processing Review, Neighbors, Connected Components, and Distance, 1998–2004.
[14]
R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, 3rd edition, 2002.
[15]
S. Venkatesan and S. S. R. Madane, “Face recognition system with genetic algorithm and ANT colony optimization,” International Journal of Innovation, Management and Technology, vol. 1, no. 5, 2010.
[16]
D. Hearn and M. P. Baker, Computer graphics C version.
[17]
G. Beylkin, R. Coifman, and V. Rokhlin, “Fast wavelet transforms and numerical algorithms I,” Communications on Pure and Applied Mathematics, vol. 44, no. 2, pp. 141–183, 1991.
[18]
E. J. Stollnitz, T. D. DeRose, and D. H. Salestin, “Wavelets for computer graphics: a primer, part 1,” IEEE Computer Graphics and Applications, vol. 15, no. 3, pp. 76–84, 1995.
[19]
M. K. Bhowmik, D. Bhattacharjee, D. K. Basu, and M. Nasipuri, “A comparative study on fusion of visual and thermal face images at different pixel level,” International Journal of Information Assurance and Security, no. 1, pp. 80–86, 2011.
[20]
J. Heo, “Fusion of Visual and Thermal Face Recognition Techniques: A Comparative Study,” The University of Tennessee, Knoxville, Tenn, USA, October 2003, http://imaging.utk.edu/publications/papers/dissertation.
[21]
Z. Yin and A. A. Malcolm, “Thermal and Visual Image Processing and Fusion,” SIMTech Technical Report, 2000.
[22]
M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71–86, 1991.
[23]
M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586–591, June 1991.
[24]
T. Ojala, M. Pietik？inen, and D. Harwood, “Performance evaluation of texture measures with classification based on Kullback discrimination of distributions,” in Proceedings of the 12th International Conference on Pattern Recognition (ICPR '94), vol. 1, pp. 582–585, 1994.
[25]
T. Ojala, M. Pietik？inen, and D. Harwood, “A comparative study of texture measures with classification based on feature distributions,” Pattern Recognition, vol. 29, no. 1, pp. 51–59, 1996.
[26]
Lin and Lee, Neural Fuzzy Systems, Prentice Hall International, 1996.
[27]
J. Haddadnia, K. Faez, and M. Ahmadi, “An efficient human face recognition system using Pseudo Zernike Moment Invariant and radial basis function neural network,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 17, no. 1, pp. 41–62, 2003.
