Robust Human Detection under Occlusion by Integrating Face and Person Detectors

Human detection under occlusion is a challenging problem in computer vision. We address this problem through a framework which integrates face detection and person detection. We first investigate how the response of a face detector is correlated with the response of a person detector. From these observations, we formulate hypotheses that capture the intuitive feedback between the responses of face and person detectors and use it to verify if the individual detectors' outputs are true or false. We illustrate the performance of our integration framework on challenging images that have considerable amount of occlusion, and demonstrate its advantages over individual face and person detectors.