Gait is an emerging biometric showing promise in its use. Most research focusses on fronto-parallel (FP) gait where people walk across a camera. In this paper, we present an original analysis, presenting the case for the use of fronto-normal (FN) gait where motion is towards a camera. In FN gait, the image projected on a camera sensor will get larger, in a looming effect. This affects the data in a nonlinear and non stationary way, which will further complicate analysis of movement. By catering for this effect, we present new insights into perspective motion compensation for FN gait. Using an existing database which uses coloured markers, we compare two methods of compensation for looming. Initial examination of the resulting data shows a significant result, that fundamentally different approaches may be used for time series analyses on the a set of FN gait data. This opens up new avenues for biometric research in gait recognition.

This paper introduces a new set of orthogonal moment functions based on the discrete Tchebichef polynomials. The Tchebichef moments can be effectively used as pattern features in the analysis of two-dimensional images. The implementation of the moments proposed in this paper does not involve any numerical approximation, since the basis set is orthogonal in the discrete domain of the image coordinate space. This property makes Tchebichef moments superior to the conventional orthogonal moments such as Legendre moments and Zernike moments, in terms of preserving the analytical properties needed to ensure information redundancy in a moment set. The paper also details the various computational aspects of Tchebichef moments and demonstrates their feature representation capability using the method of image reconstruction.