Clothing-invariant human gait recognition using an adaptive outlier detection method

Ghebleh, Abbas; Moghaddam, Mohsen Ebrahimi

doi:10.1007/s11042-017-4712-z

Cited by 17 publications

(7 citation statements)

References 20 publications

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“…When people wear different clothes, part of their silhouettes may be quite different from their normal size, and can be treated as outliers. For example, Ghebleh and Moghaddam [128] studied the GEIs of the same person with different clothes. The results showed that the distance values in rows affected by clothes were higher than the distance values in other rows, and could be treated as outliers.…”

Section: Outliers Caused By Clothesmentioning

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: Outliers Caused By Clothesmentioning

confidence: 99%

A Survey on Gait Recognition

2018

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“…Heel striking the ground and the toe off the ground mark the beginning of the stance and swing phases, respectively. (23) During the walking cycle, each leg in turn supports the person and moves him or her forward. As shown in Fig.…”

Section: Division Of Gait Phasementioning

confidence: 99%

Recognition of the Gait Phase Based on New Deep Learning Algorithm Using Multisensor Information Fusion

Wang¹,

Yan²,

Jiang³

2019

Sensors and Materials

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Gait phase recognition is an effective method of analyzing human motion and behavior that can be very meaningful in people's daily life, especially when struggling with assisted rehabilitation. In this paper, a new algorithm that can recognize a human gait phase more accurately is proposed. The new gait phase recognition algorithm is based on a deep memory convolutional neural network (DM-CNN) using multiple sensor fusion. We used the plantar pressure sensor array and acceleration sensor array gait data, and then extracted the gait features using the DM-CNN. The measured data of the continuous gait cycle were divided into unit steps, and the data were analyzed and preprocessed. Then, a feature map of each sensor array was extracted by constructing a separate DM-CNN. Finally, each feature map was combined into a fully connected network, and a memory function was introduced to simulate historical behavior. We then tested the algorithm on the phases of a gait cycle and compared the evaluation indicators of each phase. In the experiment, we compared single-mode and multimode recognition results, and compared those with the new hidden Markov model (N-HMM), K-nearest neighbor (KNN), and hidden Markov model (HMM) algorithms. The experimental results show that when the multisensor data are fused, the average recognition accuracy can reach 97.1%, which is higher than those of the other algorithms and improves the recognition of a human gait phase. The accurate recognition of human gait can provide a better theoretical basis for the design of exoskeleton robot control strategies.

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“…Whytock et al [103] proposed a novel bolt-on module enabling to improve the robustness using various single compact 2D gait representations including GEI. Recently, Ghebleh and Ebrahimi [110] introduced an adaptive outlier detection method to address the effects of clothing issue. Learning methods are robust and able to achieve good accuracy.…”

Section: Gait Parts Feature-based Representationsmentioning

confidence: 99%

“…Zhang et al [123] [135] introduced the concept of accumulated flow image and edge-masked active energy image able to produce distinctive features for classification. Lee et al [136] proposed a combination of spatio-temporal approach and texture descriptors to extract discriminative gait features named [88] 2010 anatomical properties • Choudhury and Tjahjadi, [89] 2015 anatomical properties • Verlekar et al [90] 2017 anatomical properties • Aggarwal and Vishwakarma [91] 2017 anatomical properties • Li and Chen [92] 2013 self-defined • Iwashita et al [93] 2013 self-defined • Gabriel et al [94] 2013 self-defined • Islam et al [95] 2013 self-defined • Nandy et al [96] 2016 self-defined • Lishani et al [97] 2017 self-defined • Bashir et al [98] 2008 wrapper • Dupuis et al [99] 2013 random forest • Rida et al [100] 2014 wrapper • Rida et al [101] 2015 wrapper • Rokanujjaman et al [102] 2015 wrapper • Whytock et al [103] 2015 bolt-on module • Rida et al [104] 2016 wrapper • Rida et al [105,106] 2016 group fused Lasso • Rida et al [78] 2016 SD • Alotaibi and Mahmood [107,108] 2016 Gini impurity • Issac et al [109] 2017 genetic algorithm • Ghebleh and Ebrahimi [110] 2017 adaptive outlier detection • Liang et al [111] 2016 cloth proportion transient binary patterns. Lee et al [137] applied HOG to timesliced averaged motion history image in order to extract discriminative features.…”

Section: Clothing Robust Feature Representationsmentioning

confidence: 99%

Robust gait recognition: a comprehensive survey

2018

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Gait recognition has emerged as an attractive biometric technology for the identification of people by analysing the way they walk. However, one of the main challenges of the technology is to address the effects of inherent various intra-class variations caused by covariate factors such as clothing, carrying conditions, and view angle that adversely affect the recognition performance. The main aim of this survey is to provide a comprehensive overview of existing robust gait recognition methods. This is intended to provide researchers with state of the art approaches in order to help advance the research topic through an understanding of basic taxonomies, comparisons, and summaries of the state-of-the-art performances on several widely used gait recognition datasets.

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Clothing-invariant human gait recognition using an adaptive outlier detection method

Cited by 17 publications

References 20 publications

A Survey on Gait Recognition

A Survey on Gait Recognition

Recognition of the Gait Phase Based on New Deep Learning Algorithm Using Multisensor Information Fusion

Robust gait recognition: a comprehensive survey

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