WEAKLY SUPERVISED MULTISCALE-INCEPTION LEARNING FOR WEB-SCALE FACE RECOGNITION

Supervised deep learning models like convolutional neural network (CNN) have shown very promising results for the face recognition problem, which often require a huge number of labeled face images Since manually labeling a large training set is a very difficult and time-consuming task, it is very beneficial if the deep model can be trained from face samples with only weak annotations. In this paper, we propose a general framework to train a deep CNN model with weakly labeled facial images that are available on the Internet. Specifically, we first design a deep Multiscale-Inception CNN (MICNN) architecture to exploit the multi-scale information for face recognition. Then, we train an initial MICNN model with only a limited number of labeled samples. After that, we propose a dual-level sample selection strategy to further fine-tune the MICNN model with the weakly labeled samples from both the sample level and class level, which aims to skip outliers and select more samples from confusing class pairs during training Extensive experimental results on the LFW and YTF benchmarks demonstrate the effectiveness of the proposed method.