关键词: 深度学习, 卷积神经网络, 人脸追踪, 边框回归

Abstract: Due to illumination variation, occlusion and other reasons, face tracking has challenges in the real world. A method based on Convolutional Neural Networks（CNN） for face tracking, which adopts improved siamese CNN, is presented. Taking paired image areas as input, the distance as output which evaluates the similarity of that image areas pair, the siamese CNN is trained end-to-end on the OTB dataset. Further, the bounding-box regression is applied to improve target localization accuracy. The results show that the proposed siamese CNN is superior to basic convolutional neural networks and achieves better performance on face tracking.

Key words: deep learning, convolutional neural networks, face tracking, bounding box regression