Localized Deep Norm-CNN Structure for Face Verification

Face verification is still a big challenging problem due to the different image conditions such as expression, pose, and illumination. To address these challenges, we propose a new Deep Leaning structure called Localized Deep-Norm CNN. Our model focuses on finding the correlations of features inside the sub region of each learning face by adding a localized feature normalization layer. The model can recover all the important correlated features of face images. Intuitively, the Localized Deep-Norm CNN model mimics the primary visual context of the learned face image by combining the localized extracted features representations. The local relational face features are extracted and normalized by assigning each sub-block to a local CNN model. Then, the global features are constructed by combining these localized high-level features to one fully connected layer to produce the final feature space of 4608d dimensions. In our model, two different optimization techniques are proposed to optimize the loss functions. The first optimization modifies the SoftMax loss function by using a cosine similarity metric instead of Euclidean inner-product layer. The second optimization is done by combining different loss functions with different metric learning. Our model achieves robustness accuracy of 99.19% in characterizing the similarity of multiple faces which is 0.16% improving on the LFW performance results.