Temporal Feature Extraction Robust to Headwear Variations for Person Identification Using Body Sway

Abstract

We propose a method for extracting temporal features robust to headwear variations for person identification using the video sequences of body sway. When people put on headwear such as caps and helmets, their head shapes, observed from an overhead camera, change dramatically depending on the type of headwear. The existing method cannot obtain high accuracy of person identification in situations where the head shapes change because their features are directly affected by the headwear variations. We perform a learning-based low-pass filter for the time-series signal of head center positions representing body sway to extract our temporal features robust to the headwear variations. Experimental results show that our temporal features significantly improved the accuracy of person identification when the headwear variations occur, compared to the existing features.