Towards Frame-Level Person Identification Using Kinect Skeleton Data with Deep Learning

Zhao, Wenbing

doi:10.1109/ssci50451.2021.9659996

Cited by 2 publications

(4 citation statements)

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“…The focus of these studies lies on the design, recognition, extraction, and matching performance of distinctive features [ 96 , 97 ]. Rather than a single measurement, they deal with digital anthropometric pattern [ 98 ], i.e. a set of digital features representing lengths and widths defined by keypoints commonly referred to as a skeleton in the literature [ 99 ].…”

Section: Review Of Existing Researchmentioning

confidence: 99%

“…Refs. [ 98 , 101 , 102 ], often using Microsoft Kinect [ 37 , 97 , 100 , [103] , [104] , [105] , [106] , [107] , [108] , [109] , [110] , [111] , [112] , [113] , [114] , [115] , [116] , [117] ]for depth sensing or drawing from 3D models, Euclidean distances based on anthropometric survey data [ 118 , 119 ](e.g. CAESAR [ 23 ] [ 96 , [120] , [121] , [122] ])) or 2D as well as 3D pose estimation frameworks [ [123] , [124] , [125] , [126] , [127] , [128] , [129] , [130] , [131] , [132] , [133] , [134] ].…”

Section: Review Of Existing Researchmentioning

confidence: 99%

“…CAESAR [ 23 ] [ 96 , [120] , [121] , [122] ])) or 2D as well as 3D pose estimation frameworks [ [123] , [124] , [125] , [126] , [127] , [128] , [129] , [130] , [131] , [132] , [133] , [134] ]. Notably, only few publications propose anthropometric patterns as biometric features for identification on their own [ 98 , 105 , 116 , 131 ]. Instead they are usually combined with other descriptors like body shape [ 97 ] or gait [ 101 , 135 , 136 ].…”

Section: Review Of Existing Researchmentioning

confidence: 99%

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Reconsideration of Bertillonage in the age of digitalisation: Digital anthropometric patterns as a promising method for establishing identity

Heuschkel,

Labudde

2024

Forensic Science International: Synergy

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Section: Review Of Existing Researchmentioning

confidence: 99%

Section: Review Of Existing Researchmentioning

confidence: 99%

Section: Review Of Existing Researchmentioning

confidence: 99%

See 1 more Smart Citation

Reconsideration of Bertillonage in the age of digitalisation: Digital anthropometric patterns as a promising method for establishing identity

Heuschkel,

Labudde

2024

Forensic Science International: Synergy

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“…In this paper, we focus on studies that have employed deep-learning models for detection. Given sufficient training data, deep-learning models typically attain better performance than traditional machine-learning models, as we have demonstrated previously [15]. We further limit the related works to those that have adopted the same taxonomy on smart contract vulnerabilities [5] (Table 1).…”

Section: Related Workmentioning

confidence: 99%

The Impact of Input Types on Smart Contract Vulnerability Detection Performance Based on Deep Learning: A Preliminary Study

Aldyaflah,

Zhao,

Yang

et al. 2024

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Stemming vulnerabilities out of a smart contract prior to its deployment is essential to ensure the security of decentralized applications. As such, numerous tools and machine-learning-based methods have been proposed to help detect vulnerabilities in smart contracts. Furthermore, various ways of encoding the smart contracts for analysis have also been proposed. However, the impact of these input methods has not been systematically studied, which is the primary goal of this paper. In this preliminary study, we experimented with four common types of input, including Word2Vec, FastText, Bag-of-Words (BoW), and Term Frequency–Inverse Document Frequency (TF-IDF). To focus on the comparison of these input types, we used the same deep-learning model, i.e., convolutional neural networks, in all experiments. Using a public dataset, we compared the vulnerability detection performance of the four input types both in the binary classification scenarios and the multiclass classification scenario. Our findings show that TF-IDF is the best overall input type among the four. TF-IDF has excellent detection performance in all scenarios: (1) it has the best F1 score and accuracy in binary classifications for all vulnerability types except for the delegate vulnerability where TF-IDF comes in a close second, and (2) it comes in a very close second behind BoW (within 0.8%) in the multiclass classification.

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Towards Frame-Level Person Identification Using Kinect Skeleton Data with Deep Learning

Cited by 2 publications

References 30 publications

Reconsideration of Bertillonage in the age of digitalisation: Digital anthropometric patterns as a promising method for establishing identity

Reconsideration of Bertillonage in the age of digitalisation: Digital anthropometric patterns as a promising method for establishing identity

The Impact of Input Types on Smart Contract Vulnerability Detection Performance Based on Deep Learning: A Preliminary Study

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