Individual identification using a gait dynamics graph

Deng, Muqing; Zheng, Tongjia

doi:10.1016/j.patcog.2018.06.002

Cited by 13 publications

(6 citation statements)

References 51 publications

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“…Appearance-based methods directly learn spatio-temporal features from the raw silhouettes in gait sequence by Convolutional Neural Network (CNN) [27], and then judge the subject identity of the gait sequence by feature matching. Model-based methods first model the raw silhouettes in gait sequence, then use a new way to express the original silhouettes [28], and learn their spatio-temporal features. A representative model-based method is JointsGait [29], which uses the human joints of raw gait silhouettes to create gait graph structure and then extracts the spatio-temporal features from the gait graph structure by Graph Convolutional Network (GCN) [30].…”

Section: Related Workmentioning

confidence: 99%

Spatio-temporal Gait Feature with Adaptive Distance Alignment

Chen¹,

Zhao²,

Li³

2022

Preprint

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Gait recognition is an important recognition technology, because it is not easy to camouflage and does not need cooperation to recognize subjects. However, there are still serious challenges in gait recognition, that is, people with similar walking posture are often recognized incorrectly. In this paper, We try to increase the discrimination of extraced gait features of different subjects to increase the recognition efficiency of subjects with similar walking posture. It includes the optimization of network structure and the refinement of extracted gait features. So our method is proposed, it consists of Spatio-temporal Feature Extraction (SFE) and Adaptive Distance Alignment (ADA), which SFE uses Temporal Feature Fusion (TFF) and Fine-grained Feature Extraction (FFE) to effectively extract the spatio-temporal features from raw silhouettes, ADA uses a large number of unlabeled gait data in real life as a benchmark to refine the extracted spatio-temporal features to make them have low inter-class similarity and high intra-class similarity. Extensive experiments on mini-OUMVLP and CASIA-B have proved that we have a good result than some state-ofthe-art methods.

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Section: Related Workmentioning

confidence: 99%

Spatio-temporal Gait Feature with Adaptive Distance Alignment

Chen¹,

Zhao²,

Li³

2022

Preprint

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“…This method uses Fuzzy set theory and gait information image-sigmoid feature. Deng [18] explains the new method that gait dynamics graph (GDG) for human identification. He found that these approaches produce new gait data representations for identification from a series of gait sequences, substantially reducing the data size while yet maintaining the unique characteristics of human walking.…”

Section: Literature Reviewmentioning

confidence: 99%

A Survey on Learning-Based Gait Recognition for Human Authentication in Smart Cities

Singh

Dwivedi

2021

Intelligent Sustainable Systems

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Smart cities include good infrastructure, better transportation, connectivity, security, etc. Human identification is a part of providing security to societies. Artificial intelligence plays an important role in this objective. In this paper, we have presented a state-of-the-art on different machine learning techniques used for the identification of human beings according to their movement. We found that there are various machine learning techniques, viz., support vector machine (SVM), k-nearest neighbor (k-NN), convolutional neural network (CNN), Fuzzy set theory, Discrete Fourier transform which are used for human authentication. We presented a comparative study of such schemes and provided our major findings of the survey. We observed that under certain conditions, neural network-based techniques are performing better than the other existing schemes of gait-based human authentication.

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“…Most successful GRBU methods have good results in fixed scenarios with limited conditions. Since human walking and body movement postures are affected by various factors as already mentioned, the generalization and recognition rate of the gait behaviour recognition algorithm still needs to be greatly improved [25]. Especially in 3D gait modelling, less research has been done, which resulted in a lack of an effective way to describe 3D gait perception, and restricted the construction of a related 3D gait cognitive computing model.…”

Section: Related Workmentioning

confidence: 99%

Gait Recognition and Understanding Based on Hierarchical Temporal Memory Using 3D Gait Semantic Folding

Luo

Tjahjadi

2020

Sensors

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Gait recognition and understanding systems have shown a wide-ranging application prospect. However, their use of unstructured data from image and video has affected their performance, e.g., they are easily influenced by multi-views, occlusion, clothes, and object carrying conditions. This paper addresses these problems using a realistic 3-dimensional (3D) human structural data and sequential pattern learning framework with top-down attention modulating mechanism based on Hierarchical Temporal Memory (HTM). First, an accurate 2-dimensional (2D) to 3D human body pose and shape semantic parameters estimation method is proposed, which exploits the advantages of an instance-level body parsing model and a virtual dressing method. Second, by using gait semantic folding, the estimated body parameters are encoded using a sparse 2D matrix to construct the structural gait semantic image. In order to achieve time-based gait recognition, an HTM Network is constructed to obtain the sequence-level gait sparse distribution representations (SL-GSDRs). A top-down attention mechanism is introduced to deal with various conditions including multi-views by refining the SL-GSDRs, according to prior knowledge. The proposed gait learning model not only aids gait recognition tasks to overcome the difficulties in real application scenarios but also provides the structured gait semantic images for visual cognition. Experimental analyses on CMU MoBo, CASIA B, TUM-IITKGP, and KY4D datasets show a significant performance gain in terms of accuracy and robustness.

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Individual identification using a gait dynamics graph

Cited by 13 publications

References 51 publications

Spatio-temporal Gait Feature with Adaptive Distance Alignment

Spatio-temporal Gait Feature with Adaptive Distance Alignment

A Survey on Learning-Based Gait Recognition for Human Authentication in Smart Cities

Gait Recognition and Understanding Based on Hierarchical Temporal Memory Using 3D Gait Semantic Folding

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