Abstract
AbstractGait recognition aims to recognize one subject by the way she/he walks without alerting the subject, which has drawn increasing attention. Recently, gait recognition can be represented using various data modalities, such as RGB, skeleton, depth, infrared data, acceleration, gyroscope, .etc., which have various advantages depending on the application scenarios. In this paper, we present a comprehensive survey of recent progress in gait recognition methods based on the type of input data modality. Specifically, we review commonly-used gait datasets with different gait data modalities, following with effective gait recognition methods both for single data modality and multiple data modalities. We also present comparative results of effective gait recognition approaches, together with insightful observations and discussions.KeywordsGait recognitionSensorDeep learningData modality
Published Version
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