Biometric recognition by gait: A survey of modalities and features

The scientific literature on automated gait analysis for human recognition has grown dramatically over the past 15 years. A number of sensing modalities including those based on vision, sound, pressure, and accelerometry have been used to capture gait information. For each of these modalities, a number of methods have been developed to extract and compare human gait information, resulting in different sets of features. This paper provides an extensive overview of the various types of features that have been utilized for each sensing modality and their relationship to the appearance and biomechanics of gait. The features considered in this work include (a) static and dynamic (temporal) features; (b) model-based and model-free visual features; (c) ground reaction force-based and finely resolved underfoot pressure features; (d) wearable sensor features; and (e) acoustic features. We also review the factors that impact gait recognition, and discuss recent work on gait spoofing and obfuscation. Finally, we enumerate the challenges and open problems in the field of gait recognition.