Face Recognition: Would Going Back to Functional Nature Be a Good Idea?
Traditional appearance-based methods for face recognition represent raw face images of size u ×v as vectors in a u×v-dimensional space. However in practice, this space can be too large to perform classification. For that reason, dimensionality reduction techniques are usually employed. Most of those traditional approaches do not take advantage of the spatial correlation of pixels in the image, considering them as independent. In this paper, we proposed a new representation of face images that takes into account the smoothness and continuity of the face image and at the same time deals with the dimensionality of the problem. This representation is based on Functional Data Analysis so, each face image is represented by a function and a recognition algorithm for functional spaces is formulated. The experiments on the AT&T and Yale B facial databases show the effectiveness of the proposed method.
KeywordsFace recognition functional data analysis biometrics
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