Robust gait recognition from extremely low frame-rate videos

Yu, Guan; Li, Chang-Tsun; Das, Mita

doi:10.1109/iwbf.2013.6547319

Cited by 14 publications

(29 citation statements)

References 12 publications

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“…Considering the demand for online identification, the proposed method does not filter raw skeleton data. In [15], Guan et al have verified that the random subspace method is robust to the occupied features, even for data with acquisition errors. Hence, we used the random subspace method to select the features.…”

Section: Discussionmentioning

confidence: 99%

Relative distance features for gait recognition with Kinect

Yang

Dou

et al. 2016

Journal of Visual Communication and Image Representation

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Gait and static body measurement are important biometric technologies for passive human recognition. Many previous works argue that recognition performance based completely on the gait feature is limited. The reason for this limited performance remains unclear. This study focuses on human recognition with gait feature obtained by Kinect and shows that gait feature can effectively distinguish from different human beings through a novel representation --relative distance-based gait features.Experimental results show that the recognition accuracy with relative distance features reaches up to 85%, which is comparable with that of anthropometric features. The combination of relative distance features and anthropometric features can provide an accuracy of more than 95%. Results indicate that the relative distance feature is quite effective and worthy of further study in more general scenarios (e.g., without Kinect).

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Section: Discussionmentioning

confidence: 99%

Relative distance features for gait recognition with Kinect

Yang

Dou

et al. 2016

Journal of Visual Communication and Image Representation

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“…From a quality-dependent score-level fusion viewpoint, it is particularly interesting to introduce matchers with different sensitivities into the quality measures that were used in this paper (i.e., SR and TR). For example, gait feature representations that are encoded with more temporal and/or motion information (e.g., [67,68] may be sensitive to TR variations (i.e., yielding higher accuracies for higher TRs), while those that are encoded with more static (shape) information (e.g., [69]) may be insensitive to the TR.…”

Section: Discussion and Future Workmentioning

confidence: 99%

MultiQ: single sensor-based multi-quality multi-modal large-scale biometric score database and its performance evaluation

Uddin

Muramatsu

Kimura

et al. 2017

IPSJ T Comput Vis Appl

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Single sensor-based multi-modal biometrics is a promising approach that offers simple system construction, low cost, and wide applicability to real situations such as CCTV footage-based criminal investigations. In multi-modal biometrics, fusion at the score-level is a popular and promising approach, and data qualities that affect the matching score of each modality are often incorporated as a quality-dependent score-level fusion framework. This paper presents a very large-scale single sensor-based multi-quality multi-modal biometric score database called MultiQ Score Database version 2 to advance the research into evaluation, comparison, and benchmarking of score-level fusion approaches using both quality-independent and quality-dependent protocols. We extracted gait, head, and height modalities from the OU-ISIR Gait Database and introduce spatial resolution (SR), temporal resolution (TR) and view as quality measures that significantly affect biometric system performance. We considered seven and 10 scaling factors for SR and TR, respectively, with four view variations. We then constructed a database comprising approximately 4 million genuine and 7.5 billion imposter score databases. To evaluate this database, we set two different protocols, and provided a set recognition accuracy for state-of-the-art approaches using protocols for both quality-independent and quality-dependent schemes. This database and the evaluation results will be beneficial for score-level fusion research. Additionally, we provide detailed analysis of the recognition accuracies associated with gait, head, and height modalities in different spatial/temporal resolutions and views. These analyses may be useful in criminal investigation research.

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“…They combined a large number of RSM-based weak classifiers to reduce the generalization errors [21]. Experimental results suggest that RSM is robust to a large number of covariates such as shoe, (small changes in) camera viewpoint, carrying condition [21], clothing [22], speed [20], frame-rate [18], [19], etc.…”

Section: Gait Feature Extraction and Classificationmentioning

confidence: 99%

People Identification and Tracking Through Fusion of Facial and Gait Features

Wei

et al. 2014

Biometric Authentication

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Abstract. This paper reviews the contemporary (face, gait, and fusion) computational approaches for automatic human identification at a distance. For remote identification, there may exist large intra-class variations that can affect the performance of face/gait systems substantially. First, we review the face recognition algorithms in light of factors, such as illumination, resolution, blur, occlusion, and pose. Then we introduce several popular gait feature templates, and the algorithms against factors such as shoe, carrying condition, camera view, walking surface, elapsed time, and clothing. The motivation of fusing face and gait, is that, gait is less sensitive to the factors that may affect face (e.g., low resolution, illumination, facial occlusion, etc.), while face is robust to the factors that may affect gait (walking surface, clothing, etc.). We review several most recent face and gait fusion methods with different strategies, and the significant performance gains suggest these two modality are complementary for human identification at a distance.

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Robust gait recognition from extremely low frame-rate videos

Cited by 14 publications

References 12 publications

Relative distance features for gait recognition with Kinect

Relative distance features for gait recognition with Kinect

MultiQ: single sensor-based multi-quality multi-modal large-scale biometric score database and its performance evaluation

People Identification and Tracking Through Fusion of Facial and Gait Features

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