Person identification can be performed using temporal features extracted from video sequences of body sway captured by an overhead camera. We propose a method for extracting such temporal features in a manner that is robust to headwear variation. When people are wearing headwear, such as a cap or helmet, their head shapes observed by the camera change significantly according to the type of headwear. Existing methods for person identification cannot achieve high accuracy in the presence of headwear variation because the features used by existing methods are strongly affected by the changes in head shapes. To extract temporal features that are not influenced by headwear variation, we measure the time-series signals representing body sway by estimating the center positions from head shapes. Moreover, we propose a learning-based low-pass filter to remove the components that are uninformative from the frequency components of the time-series signals, while retaining the informative components. Experimental results show that our temporal features significantly enhance the accuracy of person identification in the presence of headwear variation, compared with the use of existing features.