Suppose you are given 200x200 pixel images of faces. If you are to develop a fac

Question

Suppose you are given 200x200 pixel images of faces. If you are to develop a face recognition system that first converts the faces into local binary patterns. Then it applies a PCA on the LBP features further to reduce the dimensions. Finally a neural network based classifier is used to recognize the person.

Describe the complete details of this system starting from LBP based feature collection to PCA and the neural network (giving all sizes of features collected, matrices and matrix operations involved for the PCA part, number of layers and the number of neurons in the neural network part

Explanation / Answer

Extending recognition to uncontrolled situations is a key challenge for practical face recognition systems. Finding efficient and discriminative facial appearance
descriptors is crucial for this. Most existing approaches use features of just one type. Here we argue that robust recognition requires several different kinds of
appearance information to be taken into account, suggesting the use of heterogeneous feature sets. We show that combining two of the most successful local face
representations, Gabor wavelets and Local Binary Patterns (LBP), gives considerably better performance than either alone: they are complimentary in the sense
that LBP captures small appearance details while Gabor features encode facial shape over a broader range of scales. Both feature sets are high dimensional
so it is beneficial to use PCA to reduce the dimensionality prior to normalization and integration. The Kernel Discriminative Common Vector method is then applied
to the combined feature vector to extract discriminant nonlinear features for recognition. The method is evaluated on several challenging face datasets including
FRGC 1.0.4, FRGC 2.0.4 and FERET, with promising results.

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