Face Recognition via Heuristic Deep Active Learning

Recent successes on face recognition tasks require a large number of annotated samples for training models. However, the sample-labeling process is slow and expensive. An effective approach to reduce the annotation effort is active learning (AL). However, the traditional AL methods are limited by the hand-craft features and the small-scale datasets. In this paper, we propose a novel deep active learning framework combining the optimal feature representation of deep convolutional neural network (CNN) and labeling-cost saving of AL, which jointly learns feature and recognition model from unlabeled samples with minimal annotation cost. The model is initialized by a relative small number of labeled samples, and strengthened gradually by adding much more complementary samples for retraining in a progressive way. Our method takes both high-uncertainty samples and the high-confidence samples into consideration for the stability of model. Specifically, the high-confidence samples are selected in a self-paced learning way, and they are double verified by the prior knowledge for more reliable. These high-confidence samples are labeled by estimated class directly, and our framework jointly learns features and recognition model by combining AL with deep CNN, so we name our approach as heuristic deep active learning (HDAL). We apply HDAL on face recognition task, it achieves our goal of “minimizing the annotation cost while avoiding the performance degradation”, and the experimental results on Cross-Age Celebrity Dataset (CACD) show that the HDAL outperforms other state-of-the-art approaches in both recognition accuracy and annotation cost.