Gait analysis for recognition and classification

TLDR

The study proposes a gait appearance representation for person identification and classification. The method uses moments extracted from orthogonal view video silhouettes and compares two temporal aggregation techniques for gait recognition. The simple feature vector achieves accurate person identification and gender classification across varying days, times, and lighting, with SVM yielding strong gender classification performance.

Abstract

We describe a representation of gait appearance for the purpose of person identification and classification. This gait representation is based on simple features such as moments extracted from orthogonal view video silhouettes of human walking motion. Despite its simplicity, the resulting feature vector contains enough information to perform well on human identification and gender classification tasks. We explore the recognition behaviors of two different methods to aggregate features over time under different recognition tasks. We demonstrate the accuracy of recognition using gait video sequences collected over different days and times and under varying lighting environments. In addition, we show results for gender classification based our gait appearance features using a support-vector machine.