Humans are able to recognize small number of people they know well by the way they walk. This ability represents basic motivation for using human gait as the means for biometric identification. Such biometrics can be captured at public places from a distance without subject's collaboration, awareness, and even consent. Although current approaches give encouraging results, we are still far from effective use in real-life applications. In general, methods set various constraints to circumvent the influence of covariate factors like changes of walking speed, view, clothing, footwear, and object carrying, that have negative impact on recognition performance. In this paper we propose a skeleton model based gait recognition system focusing on modelling gait dynamics and eliminating the influence of subjects appearance on recognition. Furthermore, we tackle the problem of walking speed variation and propose space transformation and feature fusion that mitigates its influence on recognition performance. With the evaluation on OU-ISIR gait dataset, we demonstrate state of the art performance of proposed methods. 1. Introduction Psychological studies showed that humans have small but significant ability to recognize people they know well by their gait. This ability has encouraged the research for using gait as the means of biometric identification. Early studies on Point Light Displays (PLD) [1], which enable isolated study of motion by removing all other contexts from observed subjects, confirmed this ability. Commonly used biometrics based on fingerprints, face, iris, and so forth have two obvious deficiencies. They perform badly at low image resolutions and need active user participation. Gait on the other hand does not suffer from these deficiencies. It can be captured with ordinary equipment without individual’s awareness or even consent. The main deficiencies of such biometrics are the unknown level of uniqueness and covariate factors that change gait characteristics. These can be external (changes of view, direction, or speed of movement, illumination conditions, weather, clothing, footwear, terrain, etc.) or internal (changes due to illness, injuries, ageing, pregnancy, etc.). Problems are also caused by uncertain measurements, occlusions, and the use of noninvasive acquiring techniques (without sensors or markers). All these negatively influence the recognition performance in real-life environment, which is still too weak for efficient use in biometry. Methods can be categorized into two main groups. Model based approaches [2–5] build the model of human
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