Abstract
Associative memory (AM) models for human faces recognition have been previously studied in psychology and neuroscience. A kernel based AM model (KAM) has been recently proposed and demonstrated with good recognition performances. KAM first forward transforms input space to a feature space and then reconstructs input from the kernel features. For a given subject, KAM uses all of the training samples to build the model, regardless what a query face image will be. This not only keeps unnecessary overhead for model building when the number of smaples is large, but also makes the model not robust when there are outliers in the training samples, for example, from occlusions or illumination. In this paper, an improved associative memory model is investigated by combining the KAM with the k–Nearest Neighbors classification algorithm. Named as k–Nearest Neighbors Associative Memory (kNN-AM), the model takes into account the closeness between a query face image and the training prototype face images. A modular scheme of applying the proposed kNN-AM to face recognition was discussed. As a multi-class classification problem, face recognition can be carried out by simply comparing which associative memory model best describe a given query face image. Results of extensive experiments on several well-known face database show that the kNN-AM has very satisfactory recognition accuracies.
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References
Abdi, H., Valentin, D., O’Toole, A.J.: A generalized autoassociator model for face processing and sex categorization: From principal components to multivariate analysis. In: Levine, D.S., Elsberry, W.R. (eds.) Optimality in biological and artificial networks? pp. 317–337. Erlbaum, Mahwah (1997)
Kohonen, T.: Correlation matrix memories. IEEE Trans. Computers 21, 353–359 (1972)
Mallat, S.: A theory of multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11, 674–693 (1989)
O’Toole, A.J., Abdi, H., Deffenbacher, K.A., Valentin, D.: A perceptual learning theory of the information in faces. In: Valentin, T. (ed.) Cognitive and Computational Aspects of Face Recognition, pp. 159–182. Routledge, London (1995)
Phillips, P.: The FERET Evaluation Methodology for Face-Recognition Algorithms IEEE Transactions on Pattern Analysis and Machine Intelligence (1999)
Sim, T., Sukthankar, R., Mullin, M., Baluja, S.: High-performance memory-based face recognition for visitor identification. Technical Report JPRC-TR-1999-001-1 (1999)
Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)
Valentin, D., Abdi, H.: Can a linear autoassociator recognize faces from new orientations? Journal of the Optical Society of America A13, 717–724 (1996)
Valentin, D., Abdi, H., Edelman, B., Posamentier, M.: What represents a face: a computational approach for the integration of physiological and psychological data. Perceptron 26, 1271–1288 (1997)
Valentin, D.: Face-Space Models of Face Recognition. In: Computational, geometric, and process perspectives on facial cognition: Contexts and challenges. Lawrence Erbaum Associates Inc., Hillsdale (1999)
Vapnik, V.N.: Statistical Learning Theory. In: Communications and Control. Wiley Series on Adaptive and Learning Systems for Signal Processing. Wiley, New York (1998)
Zhang, B., et al.: Face Recognition by Applying Wavelet Subband Representation and Kernel Associative Memory. IEEE Transactions on Neural Networks, 166–177 (January 2004)
Zhang, H., Huang, W., Huang, Z., Zhang, B.: A Kernel Autoassociator Approach to Pattern Classification. IEEE Transactions on System, Man and Cybernetics - B 35(3) (2005)
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Zhang, Bl., Miao, Y., Gupta, G. (2005). k–Nearest Neighbors Associative Memory Model for Face Recognition. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_56
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DOI: https://doi.org/10.1007/11589990_56
Publisher Name: Springer, Berlin, Heidelberg
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