A framework for gait-based recognition using Kinect

• Dynamic characteristics of gait are utilized for identity and gender recognition.
• A new publicly available dataset for gait recognition is presented.
• Our algorithm can operate extremely well with a small sample training size.
• The proposed framework follows a biologicaly inspired human motion analysis.
• The hierarchy of feature representations results in a high level description.

Gait analysis has gained new impetus over the past few years. This is mostly due to the launch of low cost depth cameras accompanied with real time pose estimation algorithms. In this work we focus on the problem of human gait recognition. In particular, we propose a modification of a framework originally designed for the task of action recognition and apply it to gait recognition. The new scheme allows us to achieve complex representations of gait sequences and thus express efficiently the dynamic characteristics of human walking sequences. The representational power of the suggested model is evaluated on a publicly available dataset where we achieved up to 93.29% identification rate, 3.1% EER on the verification task and 99.11% gender recognition rate.