Gait is a new biometric aimed to recognize a subject by the manner in which they walk. Gait has several advantages over other biometrics, most notably that it is a non-invasive and perceivable at a distance when other biometrics are obscured. We present a new area based metric, called gait masks, which provides statistical data intimately related to the gait of the subject. Early results are promising with a recognition rate of 90% on a small database of human subjects. In addition to this, we show how gait masks can also be used on subjects other than humans to provide information about the gait cycle of the subject. In this paper, we propose a novel temporal template, called Chrono-Gait Image (CGI), to describe the spatio-temporal walking pattern for human identification by gait. The CGI temporal template encodes the temporal information among gait frames via color mapping to improve the recognition performance. Our method starts with the extraction of the contour in each gait image, followed by utilizing a color mapping function to encode each of gait contour images in the same gait sequence and compositing them to a single CGI. We also obtain the CGI-based real templates by generating CGI for each period of one gait sequence and utilize contour distortion to generate the CGI-based synthetic templates. In addition to independent recognition using either of individual templates, we combine the real and synthetic temporal templates for refining the performance of human recognition. Extensive experiments on the USF HumanID database indicate that compared with the recently published gait recognition approaches, our CGI-based approach attains better performance in gait recognition with considerable robustness to gait period detection.