A Face Tracking Method in Videos Based on Convolutional Neural Networks

Abstract

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.

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“…Ren et al present a new framework for video face tracking based on convolutional neural network (CNN) and Kalman filtering algorithms. e CNN approach from course to fine has higher accuracy in complex scenes, such as face rotation, lighting changes, and occlusions [2]. Lu and Yan propose a novel algorithm based on computer vision from the perspective of both face inspection skill and face identification technical.…”