With the advances in radar technology, there is an increasing interest in automatic radar-based human gait identification. This is because radar signals can penetrate through most dielectric materials. In this paper, an image-based approach is proposed for classifying human micro-Doppler radar signatures. The time-varying radar signal is first converted into a time-frequency representation, which is then cast as a two-dimensional image. A descriptor is developed to extract micro-Doppler features from local time-frequency patches centered along the torso Doppler frequency. Experimental results based on real data collected from a 24-GHz Doppler radar showed that the proposed approach achieves promising classification performance.