We present a correspondence-free method to automatically estimate the spatio-temporal parameters of gait (stride length and cadence) of a walking person from video. Stride and cadence are functions of body height, weight, and gender and we use these biometrics for identification and verification of people. The cadence is estimated using the periodicity of a walking person. Using a calibrated camera system, the stride length is estimated by first tracking the person and estimating their distance travelled over a period of time. By counting the number of steps (again using periodicity), and assuming constant-velocity walking, we are able to estimate the stride to within 1cm for a typical outdoor surveillance configuration (under certain assumptions). With a database of 17 people and 8 samples of each, we show that a person is verified with an Equal Error Rate (EER) of 11%, and correctly identified with a probability of 40%. This method works with low-resolution images of people, and is robust to changes in lighting, clothing, and tracking errors. It is view-invariant though performance is optimal in a near fronto-parallel configuration.
