Multiview gait-based gender classification through pose-based voting

Isaac, Ebenezer R. H. P.; Elias, Susan; Rajagopalan, Srinivasan; Easwarakumar, K. S.

doi:10.1016/j.patrec.2018.04.020

Cited by 30 publications

(19 citation statements)

References 30 publications

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“…The gait experimental results for 3 males and 2 females show that gender is distinguishable by 95%. Similar results have been obtained using video sequence [19]. However, in this study, using the GRF of the five subjects, the high classification rate could be resulted from the difference in males and females body weight, posture or muscle strength.…”

Section: Discussionsupporting

confidence: 84%

Deep Learning and Sensor Fusion Methods for Studying Gait Changes Under Cognitive Load in Males and Females

Alharthi

Ozanyan

2019

Intelligent Data Engineering and Automated Learning – IDEAL 2019

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Human gait is the manner of walking in people. It is influenced by weight, age, health condition or the interaction with the surrounding environment. In this work, we study gait changes under cognitive load in healthy males and females, using machine learning methods. A deep learning model with multi-processing pipelining and back propagation techniques, is proposed for cognitive load gait analysis. The IMAGiMAT floor system enabling sensor fusion from plastic optical fiber (POF) elements, is utilized to record gait raw data on spatiotemporal ground reaction force (GRF). A deep parallel Convolutional Neural Network (CNN) is engineered for POF sensors fusion, and gait GRF classification. The Layer-Wise Relevance Propagation (LRP), is applied to reveal which gait events are relevant towards informing the parallel CNN prediction. The CNN differentiates between males and females with 95% weighted average precision, and cognitive load gait classification with 93% weighted average precision. These findings present a new hypothesis, whereas larger dataset holds promise for human activity analysis.

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Section: Discussionsupporting

confidence: 84%

Deep Learning and Sensor Fusion Methods for Studying Gait Changes Under Cognitive Load in Males and Females

Alharthi

Ozanyan

2019

Intelligent Data Engineering and Automated Learning – IDEAL 2019

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“…It is interesting to note that gait analysis is utilized to classify a person's gender based on their gait [26]. Furthermore, attempts to identify a person's emotional state, such as pride, happiness, fear and anger, have been based on gait [27].…”

Section: B Applications Of Gait Analysismentioning

confidence: 99%

Deep Learning for Monitoring of Human Gait: A Review

2019

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The essential human gait parameters are briefly reviewed, followed by a detailed review of the state of the art in deep learning for the human gait analysis. The modalities for capturing the gait data are grouped according to the sensing technology: video sequences, wearable sensors, and floor sensors, as well as the publicly available datasets. The established artificial neural network architectures for deep learning are reviewed for each group, and their performance are compared with particular emphasis on the spatiotemporal character of gait data and the motivation for multi-sensor, multi-modality fusion. It is shown that by most of the essential metrics, deep learning convolutional neural networks typically outperform shallow learning models. In the light of the discussed character of gait data, this is attributed to the possibility to extract the gait features automatically in deep learning as opposed to the shallow learning from the handcrafted gait features.

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“…In [15] [49], the researchers attempted to resolve gender classification from multiple viewpoints. In [33][34], De Zhang et al build an invariant classifier by combining the GEIs of different viewpoints into a single third-order tensor.…”

Section: Related Workmentioning

confidence: 99%

“…A final recognition task is conducted on the synthesized data. Issac et al [49] propose a method to delineate the gait instance as a sequence of poses or frames based on the fact that humans tend to assume certain poses at each part of a gait cycle. The gender of each frame is predicted, and the gender decision of a sequence is then made using majority voting.…”

Section: Related Workmentioning

confidence: 99%

Real-time and robust multiple-view gender classification using gait features in video surveillance

Dung

Nguyen

Kim

2019

Pattern Anal Applic

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It is common to view people in real applications walking in arbitrary directions, holding items, or wearing heavy coats. These factors are challenges in gait-based application methods because they significantly change a persons appearance. This paper proposes a novel method for classifying human gender in real time using gait information. The use of an average gait image (AGI), rather than a gait energy image (GEI), allows this method to be computationally efficient and robust against view changes. A viewpoint (VP) model is created for automatically determining the viewing angle during the testing phase. A distance signal (DS) model is constructed to remove any areas with an attachment (carried items, worn coats) from a silhouette to reduce the interference in the resulting classification. Finally, the human gender is classified using multiple view-dependent classifiers trained using a support vector machine. Experiment results confirm that the proposed method achieves a high accuracy of 98.8% on the CASIA Dataset B and outperforms the recent state-of-the-art methods.

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Multiview gait-based gender classification through pose-based voting

Cited by 30 publications

References 30 publications

Deep Learning and Sensor Fusion Methods for Studying Gait Changes Under Cognitive Load in Males and Females

Deep Learning and Sensor Fusion Methods for Studying Gait Changes Under Cognitive Load in Males and Females

Deep Learning for Monitoring of Human Gait: A Review

Real-time and robust multiple-view gender classification using gait features in video surveillance

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