Color-mapped contour gait image for cross-view gait recognition using deep convolutional neural network

Linda, G. Merlin; Themozhi, G.; Bandi, Sudheer Reddy

doi:10.1142/s0219691319410121

Cited by 14 publications

(6 citation statements)

References 49 publications

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“…e results show that the results of the model optimization and the CPAFnet depth model for CPAF data set have a good practical significance for intelligent identification of agricultural and forest pests. Linda et al [16] proposed a color-mapped contour gait image (CCGI). e first contour in each gait image sequence is extracted by the CORF contour tracking algorithm.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network

Wang

2021

Computational Intelligence and Neuroscience

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Modern urban landscape is a simple ecosystem, which is of great significance to the sustainable development of the city. This study proposes a landscape information extraction model based on deep convolutional neural network, studies the multiscale landscape convolutional neural network classification method, constructs a landscape information extraction model based on multiscale CNN, and finally analyzes the quantitative effect of deep convolutional neural network. The results show that the overall kappa coefficient is 0.91 and the classification accuracy is 93% by calculating the confusion matrix, production accuracy, and user accuracy. The method proposed in this study can identify more than 90% of water targets, the user accuracy and production accuracy are 99.78% and 91.94%, respectively, and the overall accuracy is 93.33%. The method proposed in this study is obviously better than other methods, and the kappa coefficient and overall accuracy are the best. This study provides a certain reference value for the quantitative evaluation of modern urban landscape spatial scale.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network

Wang

2021

Computational Intelligence and Neuroscience

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“…The optical flow excludes the static part of GEI having high pixel intensities and represents only the dynamic part with high intensities of GEI. Similarly, Linda et al [ 24 ] proposed color-mapped contour gait images (CCGIs) and deep CNN for cross-view gait recognition. CCGIs is helpful in discriminating temporal information in human walking sequences.…”

Section: Literature Reviewmentioning

confidence: 99%

A Smart Surveillance System for Uncooperative Gait Recognition Using Cycle Consistent Generative Adversarial Networks (CCGANs)

Alsaggaf

Mehmood

Khairullah

et al. 2021

Computational Intelligence and Neuroscience

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Surveillance remains an important research area, and it has many applications. Smart surveillance requires a high level of accuracy even when persons are uncooperative. Gait Recognition is the study of recognizing people by the way they walk even when they are unwilling to cooperate. It is another form of a behavioral biometric system in which unique attributes of an individual’s gait are analyzed to determine their identity. On the other hand, one of the big limitations of the gait recognition system is uncooperative environments in which both gallery and probe sets are made under different and unknown walking conditions. In order to tackle this problem, we propose a deep learning-based method that is trained on individuals with the normal walking condition, and to deal with an uncooperative environment and recognize the individual with any dynamic walking conditions, a cycle consistent generative adversarial network is used. This method translates a GEI disturbed from different covariate factors to a normal GEI. It works like unsupervised learning, and during its training, a GEI disrupts from different covariate factors of each individual and acts as a source domain while the normal walking conditions of individuals are our target domain to which translation is required. The cycle consistent GANs automatically find an individual pair with the help of the Cycle Loss function and generate the required GEI, which is tested by the CNN model to predict the person ID. The proposed system is evaluated over a publicly available data set named CASIA-B, and it achieved excellent results. Moreover, this system can be implemented in sensitive areas, like banks, seminar halls (events), airports, embassies, shopping malls, police stations, military areas, and other public service areas for security purposes.

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“…Gait recognition methods are broadly divided into two main groups which are model-based and appearance-based or model-free approaches (BenAbdelkader, Cutler, & Davis, 2002;Yang, Larsen, Alkjaer, Simonsen, & Lynnerup, 2014). Moreover, CNNs are the most commonly used algorithm in appearance-based methods with remarkable performances (Alotaibi & Mahmood, 2017;Hawas, El-Khobby, Abd-Elnaby, & Abd El-Samie, 2019;Linda, Themozhi, & Bandi, 2020). Usually, in appearance-based methods, Gait Energy Image(GEI) (Han & Bhanu, 2005) is the most commonly used gait representation.…”

Section: Introductionmentioning

confidence: 99%

Secure Gait Recognition-Based Smart Surveillance Systems Against Universal Adversarial Attacks

Bukhari

Yasmin

Gillani

et al. 2023

Journal of Database Management

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Currently, the internet of everything (IoE) enabled smart surveillance systems are widely used in various fields to prevent various forms of abnormal behaviors. The authors assess the vulnerability of surveillance systems based on human gait and suggest a defense strategy to secure them. Human gait recognition is a promising biometric technology, but one significantly hindered because of universal adversarial perturbation (UAP) that may trigger system failure. More specifically, in this research study, the authors emphasize on sample convolutional neural network (CNN) model design for gait recognition and assess its susceptibility to UAPs. The authors compute the perturbation as non-targeted UAPs, which trigger a model failure and lead to an inaccurate label to the input sample of a given subject. The findings show that a smart surveillance system based on human gait analysis is susceptible to UAPs, even if the norm of the generated noise is substantially less than the average norm of the images. Later, in the next stage, the authors illustrate a defense mechanism to design a secure surveillance system based on human gait.

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Color-mapped contour gait image for cross-view gait recognition using deep convolutional neural network

Cited by 14 publications

References 49 publications

[Retracted] Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network

[Retracted] Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network

A Smart Surveillance System for Uncooperative Gait Recognition Using Cycle Consistent Generative Adversarial Networks (CCGANs)

Secure Gait Recognition-Based Smart Surveillance Systems Against Universal Adversarial Attacks

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