Abstract
Each individual describes unique patterns during their gait cycles. This information can be extracted from the live video stream and used for subject identification. In appearance based recognition methods, this is done by tracking silhouettes of persons across gait cycles. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. When such sensors are used for gait recognition, existing RGB appearance based methods can be extended to get a substantial gain in recognition accuracy. In this paper, this is accomplished using information fusion techniques that combine features from extracted silhouettes, used in traditional appearance based methods, and the height feature that can now be estimated using depth data. The latter is estimated during the silhouette extraction step with minimal additional computational cost. Two approaches are proposed that can be implemented easily as an extension to existing appearance based methods. An extensive experimental evaluation was performed to provide insights into how much the recognition accuracy can be improved. The results are presented and discussed considering different types of subjects and populations of different height distributions.
Highlights
Introduction and MotivationGait recognition has been studied extensively in recent years as a biometric identification method that does not require the user to approach the sensor or to interact with the recognition system
The classification algorithms k-nearest neighbors, Support Vector classification (SVC), Random forest classification (RFC), and Gaussian Naive Bayes (GNB) were tested on previously described height and gait features
The worst performance for the Height method is for the population with Similar height distribution, containing individuals with similar heights, while, for larger population sizes, the best is for Age height distribution, which contains individuals of different ages with a varied height distribution
Summary
Gait recognition has been studied extensively in recent years as a biometric identification method that does not require the user to approach the sensor or to interact with the recognition system. Depending on how the walking dynamics and body shape data are used in the recognition process, gait recognition techniques may be divided into a model and appearance based techniques. Model based techniques use explicit models to represent and track different parts of the body over time, such as legs and arms, and construct a gait signature that is later used during recognition. These techniques have high computational requirements for model construction and parameter estimation. The extraction is not computationally expensive, such methods are suitable for real-time usage
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