Speed, Cloth and Pose Invariant Gait Recognition-Based Person Identification

Semwal, Vijay Bhaskar; Mazumdar, Arghya; Jha, Ashish K.; Gaud, Neha; Bijalwan, Vishwanath

doi:10.1007/978-981-33-6518-6_3

Cited by 31 publications

(14 citation statements)

References 23 publications

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“…Identification of human through gait is the most biometric application, and researchers have made extensive studies for it by extracting feature values [ 20 ]. In literature, various machine learning and computer vision-based techniques are implemented for human gait recognition [ 21 ].…”

Section: Related Workmentioning

confidence: 99%

Human Gait Analysis: A Sequential Framework of Lightweight Deep Learning and Improved Moth-Flame Optimization Algorithm

Khan

Arshad

Damaševičius

et al. 2022

Computational Intelligence and Neuroscience

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Human gait recognition has emerged as a branch of biometric identification in the last decade, focusing on individuals based on several characteristics such as movement, time, and clothing. It is also great for video surveillance applications. The main issue with these techniques is the loss of accuracy and time caused by traditional feature extraction and classification. With advances in deep learning for a variety of applications, particularly video surveillance and biometrics, we proposed a lightweight deep learning method for human gait recognition in this work. The proposed method includes sequential steps–pretrained deep models selection of features classification. Two lightweight pretrained models are initially considered and fine-tuned in terms of additional layers and freezing some middle layers. Following that, models were trained using deep transfer learning, and features were engineered on fully connected and average pooling layers. The fusion is performed using discriminant correlation analysis, which is then optimized using an improved moth-flame optimization algorithm. For final classification, the final optimum features are classified using an extreme learning machine (ELM). The experiments were carried out on two publicly available datasets, CASIA B and TUM GAID, and yielded an average accuracy of 91.20 and 98.60%, respectively. When compared to recent state-of-the-art techniques, the proposed method is found to be more accurate.

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Section: Related Workmentioning

confidence: 99%

Human Gait Analysis: A Sequential Framework of Lightweight Deep Learning and Improved Moth-Flame Optimization Algorithm

Khan

Arshad

Damaševičius

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…In recent years, the researchers found automatic feature extractor methods, such as CNN, as a more appropriate tool. In this case, we can mention to the research studies [7,[21][22][23][24][25] that experimented with advanced feature extractor algorithms on different HAR datasets. The results indicated more appropriate detection rate in comparison to previous research studies.…”

Section: Related Workmentioning

confidence: 99%

Proposing a Fuzzy Soft‐max‐based classifier in a hybrid deep learning architecture for human activity recognition

2022

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Human Activity Recognition (HAR) is the process of identifying and analysing activities performed by a person (or persons). This paper proposes an efficient HAR system based on wearable sensors that uses deep learning techniques. The proposed HAR takes the advantage of staking Convolutional Neural Network and Long Short-Term (LSTM), for extracting the high-level features of the sensors data and for learning the time-series behaviour of the abstracted data, respectively. This paper proposed a Fuzzy Soft-max classifier for the dense layer which classifies the output of LSTM Blocks to the associated activity classes. The authors' decision for proposing this classifier was because sensor data related to the resembling human activities, such as walking and running or opening door and closing door, are often very similar to each other. For this reason, the authors expect that adding fuzzy inference power to the standard Soft-max classifier will increase its accuracy for distinguishing between similar activities. The authors were also interested in considering a post-processing module that considers activity classification over a longer period. Using the proposed Fuzzy Soft-max classifier and by the post-processing technique, the authors were able to reach the 97.03 and 85.1 rates of accuracy for the PAMAP2 and Opportunity dataset, respectively.

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“…Semwal et al (2019) explore very important technique of computer vision for object identification. It included feature extraction techniques, namely gait energy image (GEI) for cloth invariance, histogram of gradients (HOG) for multi‐view invariance, and Zernike moment with random transform for cross‐view invariance.…”

Section: Literature Surveymentioning

confidence: 99%

Human activity recognition by wireless body area networks through multi‐objective feature selection with deep learning

Boga

Kumar

2022

Expert Systems

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Wireless body area network (WBAN) is a novel technology with the incorporation of numerous types of devices, which is also employed in health monitoring applications.Human activity recognition (HAR) receives more interest in recent times along with wearable sensors. HAR system provides information about a person's identity, personality, and psychological state. In the present scenario, it becomes important to model the active learning paradigms with the help of wearable sensors for analysing human activities. Although various deep learning models and existing algorithms secure better outcomes through the sensor data analysis regarding HAR, the decision-making evaluation seems to be a complex one. The main intent of this paper is to implement the WBAN-based HAR system using the improved deep learning model. By connecting the wearable sensors, the signals are gathered regarding human activity from diverse benchmark sources. After collecting the required signals, the pre-processing of the input signals is done using artefact removal and median filtering. Further, the feature extraction is performed, which intends to extract a set of features by utilizing short-time Fourier transform (STFT) and statistical features. For reducing the feature-length, a multi-objective-based optimal feature selection is adopted. Human activities such as 'walking, walking upstairs, walking downstairs, sitting, standing, lying, and jogging' are recognized with help of selected optimal features. The optimized probabilistic neural network (PNN) and convolutional neural network (CNN) are combined and named as adaptive probabilistic-based CNN (AP-CNN). The effective performance of optimal feature selection and recognition is accomplished by incorporating the developed backward updating position-based sea lion optimization algorithm (BU-SLnO). Finally, the performance of the BU-SLnO-AP-CNN-based suggested model is analysed, which shows 1.001%, 1.237%, 0.811%, and 0.859% advanced than SLnO-AP-CNN, feed-forward-AP-CNN (FF-AP-CNN), grey wolf optimization-AP-CNN (GWO-AP-CNN), particle swarm optimization-AP-CNN (PSO-AP-CNN) when observing the dataset 1. The experimental outcomes from comparison with various classification techniques demonstrate the efficiency of the developed technique.

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Speed, Cloth and Pose Invariant Gait Recognition-Based Person Identification

Cited by 31 publications

References 23 publications

Human Gait Analysis: A Sequential Framework of Lightweight Deep Learning and Improved Moth-Flame Optimization Algorithm

Human Gait Analysis: A Sequential Framework of Lightweight Deep Learning and Improved Moth-Flame Optimization Algorithm

Proposing a Fuzzy Soft‐max‐based classifier in a hybrid deep learning architecture for human activity recognition

Human activity recognition by wireless body area networks through multi‐objective feature selection with deep learning

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