Partially Occluded Head Posture Estimation for 2D Images using Pyramid HoG Features

Jun Wu, Zongjiang Shang, Kaiwei Wang, Jiarong Zhai, Yiting Wang, Fang Xia, Wenyuan Li, Jiajia Zhang, Fan Zhang

Abstract: Automatic head posture recognition is essential in the human-centered AI applications such as counting watching people in a bus or lift advertising system. A partially-occluded head posture estimation algorithm based on a histogram of gradient orientation (HoG) in a pyramid setting is proposed. We first apply face detection to a 2D image, and then divide the detected face region into two sub-regions, mouth and eye areas, to predict whether there is an occlusion in these two sub-regions individually. According to the predicted occlusion status, pyramid HoG features are extracted from non-occluded face sub-region. Finally, a support vector machine is applied for model training. Experimental results show that our proposed method has high accuracy, speed, and robustness in terms of illumination, shadow, occlusion, and complex background. The average recognition accuracy on the CMU-PIE dataset is 95.24% (94.22% with full mouth area occlusion), and that on the CAS-PEAL-R1 dataset is 95.61%. In addition, the contribution of the eye area is significantly bigger than that of the mouth area. Our method has potential commercial values and broad application prospects for intelligent human-machine interaction.