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Deep convolution neural network with stacks of multi-scale convolutional layer block using triplet of faces for face recognition in the wild

Recently, deep convolutional neural networks have set a new trend in fields of face recognition by improving the state-of-the-art performance. By using deep neural networks, much more sophisticated and high level abstracted features can be learned automatically. In this paper, we propose a method for face recognition using multi-scale convolution layer blocks and triplets of faces in unconstrained environments. We use the ensemble of deep convolution neural networks trained on differently scaled and aligned face images. This extracts low dimensional but high-level abstraction and discriminative features for face recognition. With these features, we employ the jointly Bayesian model and transfer learning which adapts the knowledge trained from the source domain to target domain. Experiment shows that our proposed method achieves 98.33% pair-wise verification accuracy on the LFW dataset.