Abstract
This paper presents a feature extraction method from facial images and applies it to a face recognition problem. The proposed feature extraction method, called gradient-based local descriptor (GLD), first calculates the gradient information of each pixel and then forms an orientation histogram at a predetermined window for the feature vector of a facial image. The extracted features are combined with a centroid neural network with the Chi square distance measure (CNN-χ 2) for a face recognition problem. The proposed face recognition method is evaluated using the Yale face database. The results obtained in experiments imply that the CNN-χ 2 algorithm accompanied with the GLD outperforms recent state-of-art algorithms including the well-known approaches KFD (Kernel Fisher Discriminant based on eigenfaces), RDA (Regularized Discriminant Analysis), and Sobel faces combined with 2DPCA (two dimensional Principle Component Analysis) in terms of recognition accuracy.
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Huyen, N.T.B., Park, DC., Woo, DM. (2010). Gradient-based Local Descriptor and Centroid Neural Network for Face Recognition. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_25
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DOI: https://doi.org/10.1007/978-3-642-13318-3_25
Publisher Name: Springer, Berlin, Heidelberg
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