Gait Recognition Using Principal Curves and Neural Networks

Su, Hailin; Feng-gang, Huang

doi:10.1007/11760023_35

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…For example, Xiao and Yang [142] used Zernike Moments and Back Propagation (BP) Neural Network for gait recognition. And Su and Huang [143] used principal curves and neural networks for gait recognition.…”

Section: Neural Networkmentioning

confidence: 99%

A Survey on Gait Recognition

2018

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Recognizing people by their gait has become more and more popular nowadays due to the following reasons. First, gait recognition can work well remotely. Second, gait recognition can be done from low-resolution videos and with simple instrumentation. Third, gait recognition can be done without the cooperation of individuals. Fourth, gait recognition can work well while other features such as faces and fingerprints are hidden. Finally, gait features are typically difficult to be impersonated. Recent ubiquity of smartphones that capture gait patterns through accelerometers and gyroscope and advances in machine learning have opened new research directions and applications in gait recognition. A timely survey that addresses current advances is missing. In this article, we survey research works in gait recognition. In addition to recognition based on video, we address new modalities, such as recognition based on floor sensors, radars, and accelerometers; new approaches that include machine learning methods; and examine challenges and vulnerabilities in this field. In addition, we propose a set of future research directions. Our review reveals the current state-of-art and can be helpful to both experts and newcomers of gait recognition. Moreover, it lists future works and publicly available databases in gait recognition for researchers.

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Section: Neural Networkmentioning

confidence: 99%

A Survey on Gait Recognition

2018

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“…A large number of gait recognition methods have been continuously contributed, which can be roughly categorized into two groups, model-based methods [1], [4], [5], [8], [9], [13], [14], [15], [17], [18], [20], [26], [30], [31] and motionbased methods [2], [3], [6], [12], [16], [19], [21], [22], [23], [25], [27]. Model-based approaches generally aim to model human body or motion and the kinematics of joint angles.…”

Section: Introductionmentioning

confidence: 99%

Automatic Gait Recognition Using Weighted Binary Pattern on Video

Kusakunniran

et al. 2009

2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance

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Human identification by recognizing the spontaneous gait recorded in real-world setting is a tough and not yet fully resolved problem in biometrics research. Several issues have contributed to the difficulties of this task. They include various poses, different clothes, moderate to large changes of normal walking manner due to carrying diverse goods when walking, and the uncertainty of the environments where the people are walking. In order to achieve a better gait recognition, this paper proposes a new method based on Weighted Binary Pattern (WBP). WBP first constructs binary pattern from a sequence of aligned silhouettes. Then, adaptive weighting technique is applied to discriminate significances of the bits in gait signatures. Being compared with most of existing methods in the literatures, this method can better deal with gait frequency, local spatial-temporal human pose features, and global body shape statistics. The proposed method is validated on several well known benchmark databases. The extensive and encouraging experimental results show that the proposed algorithm achieves high accuracy, but with low complexity and computational time.

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