Soft biometrics, as a prescreening filter, contribute to a much smaller candidate pool and allow the overall query to perform better and faster. In this paper, we focus on the efficiency and effectiveness of several soft biometrics for surveillance applications. We propose a temporal signature in x-t slices. Such a signature has explicitly embedded body articulation and enables direct mensuration. The algorithms determine characteristics for gender, body size, height, cadence, and stride of the subject using a novel gait analysis tool. We have evaluated algorithm performance under various poses, ranges, and illuminations. Preliminary experiments have shown promising results.

Flash LADAR systems are becoming increasingly popular for robotics applications. However, they generally provide a low-resolution range image because of the limited number of pixels available on the focal plane array. In this paper, the application of image super-resolution algorithms to improve the resolution of range data is examined. Super-resolution algorithms are compared for their use on range data and the frequency-domain method is selected. Four low-resolution range images which are slightly shifted and rotated from the reference image are registered using Fourier transform properties and the super-resolution image is built using non-uniform interpolation. Image super-resolution algorithms are typically rated subjectively based on the perceived visual quality of their results. In this work, quantitative methods for evaluating the performance of these algorithms on range data are developed. Edge detection in the range data is used as a benchmark of the data improvement provided by super-resolution. The results show that super-resolution of range data provides the same advantage as image super-resolution, namely increased image fidelity.