Distance metric optimization driven convolutional neural network for age invariant face recognition

Despite the great advances in face-related works in recent years, face recognition across age remains a challenging problem. The traditional approaches to this problem usually include two basic steps: feature extraction and the application of a distance metric, sometimes common space projection is also involved. On the one hand, handling these steps separately ignores the interactions of these components, and on the other hand, the fixed-distance threshold of measurement affects the model's robustness. In this paper, we present a novel distance metric optimization driven learning approach that integrates these traditional steps via a deep convolutional neural network, which learns feature representations and the decision function in an end-to-end way. Given the labelled training images, we first generate a large number of pairs with a certain proportion of matched and unmatched pairs. For matched pairs, we try to select as many different age instances as possible for each person to learn the identification information that is not affected by age. Then, taking these pairs as input, we aim to enlarge the differences between the unmatched pairs while reducing the variations between the matched pairs, and we update the model parameters by using the mini-batch stochastic gradient descent (SGD) algorithm. Specifically, the distance matrix is used as the top fully connected layer, and the bottom layers representing the image features are integrated with it seamlessly. Thus, the image features and the distance metric can be optimized simultaneously by backward propagation. In particular, we introduce several training strategies to reduce the computational cost and overcome insufficient memory capacity. We evaluate our method on three tasks: age-invariant face identification on the MORPH database, age-invariant face retrieval on the CACD database and age-invariant face verification on CACD-VS database. The experimental results demonstrate the effectiveness of our approach. (C) 2017 Published by Elsevier Ltd.