Gait Recognition Based on Convolutional Neural Networks

Sokolova, Anastasiia D.; Konushin, Anton

doi:10.5194/isprs-archives-xlii-2-w4-207-2017

Cited by 30 publications

(19 citation statements)

References 13 publications

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“…Networks similar to this have produced the best results in previous experiments [3] in which the blocks of several consecutive OF maps were used as network inputs. However, that approach [3] did not take into account the key points of the human pose; instead, the full body was used. We will further compare this previous study with our approach in Section 4.…”

Section: Proposed Methodsmentioning

confidence: 98%

“…The OF approach was applied to the gait recognition problem in several works [3, 4, 23], which proposed a deep model using blocks of OF maps containing full bodies as inputs to predict recorded individuals. Several other deep gait recognition solutions unite neural and GEI approaches.…”

Section: Related Workmentioning

confidence: 99%

“…Unlike [3, 4] we use not just motion of the whole body to recognise the human, but consider the hierarchy of body parts from full body to exact joints and observe the motion of the points in these shrinking areas. Such an approach is novel in gait recognition and distinguishes our method among the others.…”

Section: Introductionmentioning

confidence: 99%

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Pose‐based deep gait recognition

Sokolova

Konushin

2019

IET biom.

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Human gait or walking manner is a biometric feature that allows identification of a person when other biometric features such as the face or iris are not visible. In this paper, we present a new pose-based convolutional neural network model for gait recognition. Unlike many methods that consider the full-height silhouette of a moving person, we consider the motion of points in the areas around human joints. To extract motion information, we estimate the optical flow between consecutive frames. We propose a deep convolutional model that computes pose-based gait descriptors. We compare different network architectures and aggregation methods and experimentally assess various sets of body parts to determine which are the most important for gait recognition. In addition, we investigate the generalization ability of the developed algorithms by transferring them between datasets. The results of these experiments show that our approach outperforms state-of-the-art methods.

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Section: Proposed Methodsmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

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Pose‐based deep gait recognition

Sokolova

Konushin

2019

IET biom.

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“…They achieve state-of-art results on two large gait datasets (CASIA Gait Dataset B and SZU RGB-D). Sokolova and Konushin [147] implement deep learning for gait recognition using optical flow. They compare several deep neural network architectures.…”

Section: Deep Learning In Gait Recognitionmentioning

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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“…Some of these methods consist of putting the best parameters that permit to minimize erroneous classifications on the given learning set (for instance, using error backpropagation method or optimization methods). The first application of CNN to gait recognition are in (Castro et al, 2016) and (Sokolova and Konushin, 2017). The SVM, which is first introduced by Vapnik (1999) in 1992 as a geometric-based classifier, is widely used in biometric systems because of its capability and accuracy.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Biometric Gait Recognition Based on Machine Learning Algorithms

Sayed¹

2018

Journal of Computer Science

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Abstract:It is crucial to find methods that analyze large amount of data captured by cameras and/or various sensors installed all around us. Machine learning becomes a prevailing tool in analyzing such data that signifies behavioral characteristics of human beings. Gait as an identifier for use in individual recognition systems has respective and almost certainly unique key features for each person including centroid, cycle length and step size. Gait is sometimes preeminent suited to recognition or surveillance scenarios. It might be used in the identification of females who are wearing veils in some countries without critical social issues. The objective of this project is to predict accurately one-dimensional coordinates of normalized n-component vectors representing twodimensional silhouettes in order to identify individuals at a distance without any interaction and obtrusion. Varied algorithms are further incorporated into walk pattern analysis to adoptively improve gait recognitions and classification. The results are reported reasonable identification performance as compared to several machine learning methods.

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Gait Recognition Based on Convolutional Neural Networks

Cited by 30 publications

References 13 publications

Pose‐based deep gait recognition

Pose‐based deep gait recognition

A Survey on Gait Recognition

Biometric Gait Recognition Based on Machine Learning Algorithms

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