关键词: 序列级检测, 帧级检测, 多模式特征, 随机回归森林

Abstract: This paper presents a method for individually describing the video segments with open eyes and those with blink and coarse-to-fine blink detection based on two-stage cascade model composed of sequence-level and frame-level. In sequence-level detection phase, the video segment is condensed and CNN feature is extracted. Optical flow between frames is accumulated and expressed as dynamic feature by Bag of Feature（BoF）. The above two features are fused to discover whether blinks exist in the current segment by classification. In frame-level detection phase, the blink motion is described precisely to describe every frame may contain blink by extracting multi-mode features. Eye closity is calculated by random regression forest and the precise blink localization is obtained. Experiments on different datasets show the proposed method improves the robustness of blink detection under uncontrolled real environment and reaches an increased performance in correct rate, convergence and speed are under the considerable computational complexity. Compared with the other current methods, the method has significant value in real applications.

Key words: sequence-level detection, frame-level detection, multi-mode feature, random regression forest