Nowadays, with rapid progress and interest in surveillance, the requirements of face recognition have dramatically increased. However, facial images captured by the camera are different from previously trained data. The captured images can be noisy and degraded. For solving these problems in the face recognition process, we propose a new method of extending the SVDD (support vector data description). In this chapter, we consider the problem of recognizing facial images and propose to use the SVDD-based face recognition. In the proposed method, we first solve the SVDD problem for the data belonging to the given prototype facial images, and model the data region for the normal faces as the ball resulting from the SVDD problem. Next, for each input facial image in various conditions, we project its feature vector onto the decision boundary of the SVDD ball so that it can be tailored enough to belong to the normal region. Finally, we synthesize facial images that are obtained from the preimage of the projection, and then perform face recognition. The applicability of the proposed method is illustrated via some experiments dealing with faces changed by different environments.
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Lee, SW., Lee, SW. (2008). SVDD-Based Face Reconstruction in Degraded Images. In: Ratha, N.K., Govindaraju, V. (eds) Advances in Biometrics. Springer, London. https://doi.org/10.1007/978-1-84628-921-7_17
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DOI: https://doi.org/10.1007/978-1-84628-921-7_17
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