A Face Tracking Method in Videos Based on Convolutional Neural Networks

作者：Ren, Zihan; Li, Jianwei; Zhang, Xiaoying; Yang, Shuangyuan^*; Zou, Fuhao

来源：International Journal of Pattern Recognition and Artificial Intelligence, 2018, 32(12): 1856017.

DOI：10.1142/S0218001418560177

• Face tracking
• convolution neural network
• Kalman filter

Face tracking in surveillance videos is one of the important issues in the field of computer vision and has realistic significance. In this paper, a new face tracking framework in videos based on convolutional neural networks (CNNs) and Kalman filter algorithm is proposed. The framework uses a rough-to-fine CNN to detect faces in each frame of the video. The rough-to-fine CNN method has a higher accuracy in complex scenes such as face rotation, light change and occlusion. When face tracking fails due to severe occlusion or significant rotation, the framework uses Kalman filter to predict face position. The experimental results show that the proposed method has high precision and fast processing speed.

• 出版日期2018-12
• 单位华中科技大学; 福州大学; 厦门大学