An ensemble face recognition mechanism based on three-way decisions

Shah, Asad Hussain; Ali, Bahar; Habib, Masood; Frnda, Jaroslav; Ullah, Inam; Anwar, Muhammad Shahid

doi:10.1016/j.jksuci.2023.03.016

Cited by 18 publications

(3 citation statements)

References 71 publications

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“…Threeway decisions has been widely used in various fields with great success [15][16][17]. For example, Shah et al [18] proposed an integrated face recognition mechanism based on three-way decisions for human-computer interaction to improve the accuracy of the authorization and recognition processes. This increases the value of the face recognition system by introducing a three-way decisions recognition method to enhance the accuracy of the system and reduce the number of false rejections.…”

Section: Introductionmentioning

confidence: 99%

MTTEGDM: A Moving Target Evolutionary Game Defense Model Based on Three-Way Decisions

Zhang,

Liu,

Zhang

et al. 2024

Electronics

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Aiming at the fact that the moving target defense game model fails to accurately portray attack and defense gains, resulting in bias in attack and defense games and the inability to select effective defense strategies, we construct the moving target three-way evolutionary game defense model (MTTEGDM). Firstly, the model is defined and analyzed theoretically under the premise of uncertainty and irrationality. Then, combined with the three-way decisions, the attack intention is introduced into the target network loss calculation, and a dynamic weight adjustment algorithm based on the three-way decisions is proposed to accurately characterize the attack and defense gains from a multi-attribute perspective. Finally, the evolutionary game model is used to analyze the evolution trend of the multi-stage defense strategy, so as to carry out feasible and effective defense behavior. The simulation results show that the model can accurately predict the optimal defense strategy of moving targets in different stages. Through a Monte Carlo simulation experiment, the proposed algorithm is compared with the traditional evolutionary game model, and the effectiveness and security of the proposed algorithm are verified.

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Section: Introductionmentioning

confidence: 99%

MTTEGDM: A Moving Target Evolutionary Game Defense Model Based on Three-Way Decisions

Zhang,

Liu,

Zhang

et al. 2024

Electronics

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“…For face detection, the techniques used are very varied. For example, the use of binary descriptors [5], mechanisms based on three-way decisions [6], and alignment learning [7], all of them based mainly on image and video analysis [8]. One of the most efficient techniques for extracting features through images or videos focuses on deep learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Comparison of convolutional neural network models for user’s facial recognition

Pinzón-Arenas,

Jimenez-Moreno,

Martinez Baquero

2024

IJECE

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This paper compares well-known convolutional neural networks (CNN) models for facial recognition. For this, it uses its database created from two registered users and an additional category of unknown persons. Eight different base models of convolutional architectures were compared by transfer of learning, and two additional proposed models called shallow CNN and shallow directed acyclic graph with CNN (DAG-CNN), which are architectures with little depth (six convolution layers). Within the tests with the database, the best results were obtained by the GoogLeNet and ResNet-101 models, managing to classify 100% of the images, even without confusing people outside the two users. However, in an additional real-time test, in which one of the users had his style changed, the models that showed the greatest robustness in this situation were the Inception and the ResNet-101, being able to maintain constant recognition. This demonstrated that the networks of greater depth manage to learn more detailed features of the users' faces, unlike those of shallower ones; their learning of features is more generalized. Declare the full term of an abbreviation/acronym when it is mentioned for the first time.

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“…With the rapid development of computer technology and artificial intelligence, Human Machine Interaction (HMI) has emerged as one of the most prominent research fields in contemporary times. The driving force behind HMI is our expectation that machines will become intelligent and perceptive like humans [1]. HMI refers to the process of exchanging information between humans and machines through effective dialogue.…”

Section: Introductionmentioning

confidence: 99%

Data glove-based gesture recognition using CNN-BiLSTM model with attention mechanism

Wu,

Ren,

Song

et al. 2023

PLoS ONE

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As a novel form of human machine interaction (HMI), hand gesture recognition (HGR) has garnered extensive attention and research. The majority of HGR studies are based on visual systems, inevitably encountering challenges such as depth and occlusion. On the contrary, data gloves can facilitate data collection with minimal interference in complex environments, thus becoming a research focus in fields such as medical simulation and virtual reality. To explore the application of data gloves in dynamic gesture recognition, this paper proposes a data glove-based dynamic gesture recognition model called the Attention-based CNN-BiLSTM Network (A-CBLN). In A-CBLN, the convolutional neural network (CNN) is employed to capture local features, while the bidirectional long short-term memory (BiLSTM) is used to extract contextual temporal features of gesture data. By utilizing attention mechanisms to allocate weights to gesture features, the model enhances its understanding of different gesture meanings, thereby improving recognition accuracy. We selected seven dynamic gestures as research targets and recruited 32 subjects for participation. Experimental results demonstrate that A-CBLN effectively addresses the challenge of dynamic gesture recognition, outperforming existing models and achieving optimal gesture recognition performance, with the accuracy of 95.05% and precision of 95.43% on the test dataset.

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An ensemble face recognition mechanism based on three-way decisions

Cited by 18 publications

References 71 publications

MTTEGDM: A Moving Target Evolutionary Game Defense Model Based on Three-Way Decisions

MTTEGDM: A Moving Target Evolutionary Game Defense Model Based on Three-Way Decisions

Comparison of convolutional neural network models for user’s facial recognition

Data glove-based gesture recognition using CNN-BiLSTM model with attention mechanism

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