Abstract
In this paper, a neural network based face recognition system is presented. One of the main problems encountered when using neural networks for face recognition is luck of enough training data. This is because, in most cases, only one image per subject is available. Therefore, one of our objectives is to solve the problem of luck of enough data to train neural networks. For each image we ”increase” the data available by several processes for example, mirroring of the image, using color, edges information, etc. The neural network is trained using structural learning to reduce its size. To represent the face color, the YIQ and the XYZ color spaces are used.
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References
Qing, J.: Principal Component Analysis and Neural Network Based Face Recognition. University of Chicago
Gita, S.: Face recognition: A critical look at biologically-inspired approaches. Tech. report CMU-RI-TR-00-04, Robotics Institute, Carnegie Mellon University (2000)
Desimore, R.: Face-selective cells in the temporal cortex of monkeys Journal of Cognitive Neuroscience 3(1) (1998)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1) (1991)
Wislott, L., Fellous, J., Kruger, N., Malsbnurg, C.: Face recognition by elastic bunch graph matching. IEEE Trans. PAMI 19(7) (1997)
Hancock, P., Bruce, V., Burton, M.: A comparison of two computer based face identification systems with human perception of faces. Visions Research 38, 15–16 (1998)
Sim T., Sukthankar R., Mullin M. Baluja S.:, High performance memory based face recognition for visitor identification. Technical report JPRC-TR-1999-001-1, Just Research (1999)
Fromherz T., Stucki P., Bichsel M.: A survey of face recognition, MML Tech. Rep. 97.01, Dept. Comp. Sci., Univ. of Zurich (1997)
Karungaru, S., Fukumi, M., Akamatsu, N.: Human face detection in visual scenes using neural networks. Trans. of IEEJ 122C, 995–1000 (2002)
Ishikawa, M.: Structure learning with forgetting. neural networks 9(3), 509–521 (1993)
Plataniotis, K., Venetsanopoulos, A.: Color image processing and applications, pp. 1–47. Springer, Heidelberg (2000)
Karungaru, S., Fukumi, M., Akamatsu, N.: Morphing Face Images using Automatically Specified Features. In: Proc. of IEEE/MWSCAS. Cairo, Egypt, (Dec, 2003)
Soriano, M., Marszalec, E., Pietikäinen, M.: Color correction of face images under different illuminants by RGB eigenfaces. Proc. 2nd Audio and Video Based Biometric Person Authentication Conference (AVBPA 1999), 148–153 (1999)
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Karungaru, S., Fukumi, M., Akamatsu, N. (2004). Recognizing Frontal Faces Using Neural Networks. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30133-2_139
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DOI: https://doi.org/10.1007/978-3-540-30133-2_139
Publisher Name: Springer, Berlin, Heidelberg
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