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**Extra info for Advances in Face Image Analysis: Theory and Applications**

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Cn INTRODUCTION A feature extractor and a classifier are two essential modules in a conventional image pattern recognition system. A good feature extractor of image could produce a feature representation which has more discriminant information and less correlations than the original pixel data. , the Scale-Invariant Feature Transform (SIFT) [1] and the Histogram of Oriented Gradients (HOG) [2]. On the other hand, a highly efficient classifier could perform its job well without any help of complex feature extractors.

Thus different architectures could be devised for different problems and for different GPUs. A compromise should be made between reducing the time cost of each epoch and improving the performance of each epoch, so as to make the network converges faster. In a word, CNNs have four characteristics which are listed as follows: Convolution / local receptive field is used for sharing weights. Sub-sampling / pooling is used. Network has 2-5 convolutional layers and 1-2 fully connected layers followed by.

Dnn), Dii = ∑jWij. The problem of CGE is as follows: (20) The optimal vector z is given by the minimum eigenvalue solution to the generalized eigenvalue problem: (21) The eigenvectors z1, . . , zd corresponding to the d smallest eigenvalues, yield the auxiliary matrix Z = [ z1, . . , zd]. We have Y = ( UZ)T . Y is a d × n matrix and it represents the data projection of X in d-dimensional space. SPP BASED CONSTRAINED GRAPH EMBEDDING In the section, we propose a SPP based Constrained Graph Embedding Based (SPP-CGE).