Full Body Person Identification Using the Kinect Sensor

Andersson, Virginia O.; Araújo, Ricardo M.

doi:10.1109/ictai.2014.99

Cited by 19 publications

(25 citation statements)

References 12 publications

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“…From the evaluation with different numbers of subjects, it was shown that k-NN always outperformed MLP. Andersson et al [24] extended their previous work by exploring the effect of different numbers of subjects on the person identification accuracy. In addition, to improve the accuracy, they proposed to use the anthropometric features for all limbs rather than for only a limited number of limbs.…”

Section: Related Workmentioning

confidence: 99%

“…The k-NN algorithm outperformed the other algorithms except when the number of subjects was less than 35. By utilizing the extended anthropometric features, the accuracy of k-NN increased from approximately 80% [23] to approximately 85.4% [24].…”

Section: Related Workmentioning

confidence: 99%

“…As reviewed in Section 2, the three machine-learning algorithms, k-NN, SVM, and MLP, have been widely used for person identification. In general, k-NN demonstrates better performance than the other methods [22][23][24][25]. For verification of the performance, we implemented the MLP as in previous studies [22][23][24][25], which included a single hidden layer.…”

Section: Motivationmentioning

confidence: 99%

“…The use of skeleton data created a new modality for biometric features, called the anthropometric feature, for person identification. In [22][23][24][25][26][27][28], the authors proposed the use of anthropometric features for the person identification task. In most of these studies, several machine-learning algorithms, such as k-nearest neighbor (k-NN), support vector machine (SVM), and multi-layer perceptron (MLP), were used to identify individuals using the anthropometric features.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, as long as the diversity of dataset is guaranteed to represent the person identification, it could achieve higher identification accuracy than using traditional machine learning methods. To this end, first of all, we explored the dataset that was used in previous studies [22][23][24][25]. From this investigation, we observed the overfitting that caused significant performance degradation.…”

Section: Introductionmentioning

confidence: 99%

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Human Skeleton Data Augmentation for Person Identification over Deep Neural Network

Kwon

Lee

2020

Applied Sciences

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With the advancement in pose estimation techniques, skeleton-based person identification has recently received considerable attention in many applications. In this study, a skeleton-based person identification method using a deep neural network (DNN) is investigated. In this method, anthropometric features extracted from the human skeleton sequence are used as the input to the DNN. However, training the DNN with insufficient training datasets makes the network unstable and may lead to overfitting during the training phase, causing significant performance degradation in the testing phase. To cope with a shortage in the dataset, we investigate novel data augmentation for skeleton-based person identification by utilizing the bilateral symmetry of the human body. To achieve this, augmented vectors are generated by sharing the anthropometric features extracted from one side of the human body with the other and vice versa. Thereby, the total number of anthropometric feature vectors is increased by 256 times, which enables the DNN to be trained while avoiding overfitting. The simulation results demonstrate that the average accuracy of person identification is remarkably improved up to 100% based on the augmentation on public datasets.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Human Skeleton Data Augmentation for Person Identification over Deep Neural Network

Kwon

Lee

2020

Applied Sciences

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Subject Identification Based on Gait Using a RGB-D Camera

Rocha

Fernandes

Choupina

et al. 2019

Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018)

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Deep learning-powered multimodal biometric authentication: integrating dynamic signatures and facial data for enhanced online security

Salturk,

Kahraman

2024

Neural Comput & Applic

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The significant increase in online activities in the wake of recent global events has underlined the importance of biometric person authentication on digital platforms. Although many biometric devices may be used for precise biometric authentication, acquiring the necessary technology, such as 3D sensors or fingerprint scanners, can be prohibitively expensive and logistically challenging. Addressing the demands of online environments, where access to specialized hardware is limited, this paper introduces an innovative approach. In this work, by fusing static and dynamic signature data with facial data captured through regular computer cameras, a dataset of 1750 samples from 25 individuals is constructed. Deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), and temporal convolutional networks (TCN), are employed to craft a robust multi-classification model. This integration of various deep learning algorithms has demonstrated remarkable performance enhancements in biometric authentication. This research also underscores the potential of merging dynamic and static biometric features, derived from readily available sources, to yield a high-performance recognition framework. As online interactions continue to expand, the combination of various biometric modalities holds potential for enhancing the security and usability of virtual environments.

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Full Body Person Identification Using the Kinect Sensor

Cited by 19 publications

References 12 publications

Human Skeleton Data Augmentation for Person Identification over Deep Neural Network

Human Skeleton Data Augmentation for Person Identification over Deep Neural Network

Subject Identification Based on Gait Using a RGB-D Camera

Deep learning-powered multimodal biometric authentication: integrating dynamic signatures and facial data for enhanced online security

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