People identification using shadow dynamics

People identification has numerous applications, ranging from surveillance/security to robotics. Face and body movement/ gait biometrics are the most important tools for this task. Traditional biometrics use direct observation of the body, yet in some situations a projection may offer more information than the direct signal, for example the shadow of a person observed from overhead, e.g. from an unmanned aerial vehicle, may contain more detail than the top view of the head/body. We introduced the idea of shadow biometrics, exploiting biometrics information in human shadow silhouettes as derived from video imagery; this enables "overhead biometrics", for recognition of human identity and behavior from high altitude airborne platforms using overhead video sequences. In this paper, we provide a demonstration of person identification based on gait recognition from shadow analysis. We describe compensation steps to address shadow variation with conditions of observation (sun position, etc). We define measures of shape variation, such as horizontal stripes on the silhouette, their length change in time determines frequency components (here spherical harmonics) for each gait cycle, which are used for classification by a k-nearest neighbor classifier. A correct classification rate (CCR) of 95 % was obtained. A degradation of CCR from 95 % to 75 % was observed when reduced spatial and temporal resolution from 1cm to 2cm, and from 30fps to 15fps.