Pig Face Recognition Based on Trapezoid Normalized Pixel Difference Feature and Trimmed Mean Attention Mechanism

Xu, Shuiqing; He, Qihang; Tao, Songbing; Chen, Hongtian; Chai, Yi; Zheng, Weixing

doi:10.1109/tim.2022.3232093

Cited by 16 publications

(5 citation statements)

References 55 publications

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“…Stability analysis and structural validations are the bases of the studies with some mathematical background [95] . To preserve the fairness and validity of the experiments, all the algorithms involved in the experiments were performed in the same environment [96] , [97] , [98] .…”

Section: Experiments and Resultsmentioning

confidence: 99%

Multi-threshold image segmentation using an enhanced fruit fly optimization for COVID-19 X-ray images

Hao

Huang

Heidari

et al. 2023

Biomedical Signal Processing and Control

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Section: Experiments and Resultsmentioning

confidence: 99%

Multi-threshold image segmentation using an enhanced fruit fly optimization for COVID-19 X-ray images

Hao

Huang

Heidari

et al. 2023

Biomedical Signal Processing and Control

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“…Then the generated images are trained in PGGAN and other above methods. Three datasets Face2Face, FaceSwap, and DeepFake in Dang et al (2020) and Xu et al (2022b) use a recurrent neural network with CNN and detect the manipulated frame very fast. The CNN with ResNet model ( Neves et al, 2020 ; Choi et al, 2018 ), DenseNet ( He et al, 2016 ; Sabir et al, 2019 ; Chen et al, 2020 ) and bidirectional recurrent technology is utilized to achieve higher accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

VIOLA jones algorithm with capsule graph network for deepfake detection

Venkatachalam

Trojovský

Hubálovský

2023

PeerJ Computer Science

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DeepFake is a forged image or video created using deep learning techniques. The present fake content of the detection technique can detect trivial images such as barefaced fake faces. Moreover, the capability of current methods to detect fake faces is minimal. Many recent types of research have made the fake detection algorithm from rule-based to machine-learning models. However, the emergence of deep learning technology with intelligent improvement motivates this specified research to use deep learning techniques. Thus, it is proposed to have VIOLA Jones’s (VJ) algorithm for selecting the best features with Capsule Graph Neural Network (CN). The graph neural network is improved by capsule-based node feature extraction to improve the results of the graph neural network. The experiment is evaluated with CelebDF-FaceForencics++ (c23) datasets, which combines FaceForencies++ (c23) and Celeb-DF. In the end, it is proved that the accuracy of the proposed model has achieved 94.

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“…In general, emotion recognition methods can be divided into two types depending on whether physiological or nonphysiological signals is involved [20]. Non-physiological signals, including speech, posture, and facial expression [21], are external manifestations of human emotions. Physiological signals, corresponding to the physiological reactions caused by emotions, such as eye electricity, ECG, EMG, and EEG, are human recessive emotional expressions [22].…”

Section: Introductionmentioning

confidence: 99%

“…As a result, these algorithms are incapable of detecting or extracting complicated EEG signals and can only produce general categorization outcomes [40]. In recent years, with the deep learning in natural language processing, computer vision [21], speech recognition, and other fields [41], it has shown good efficiency and flexibility [42]. More and more researchers study the emotion recognition task of EEG signals based on deep learning and try to use different EEG signal characteristics to improve the accuracy of emotion recognition.…”

Section: Introductionmentioning

confidence: 99%

Bi-Branch Vision Transformer Network for EEG Emotion Recognition

Lü

Tan

2023

IEEE Access

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Electroencephalogram (EEG) signals have emerged as an important tool for emotion research due to their objective reflection of real emotional states. Deep learning-based EEG emotion classification algorithms have made preliminary progress, but existing models struggle with capturing long-range dependence and integrating temporal, frequency, and spatial domain features to limit their classification ability. To address these challenges, this study proposes a Bi-branch Vision Transformerbased EEG emotion recognition model, Bi-ViTNet, that integrates spatial-temporal and spatial-frequency feature representations. Specifically, Bi-ViTNet is composed of spatial-frequency feature extraction branch and spatial-temporal feature extraction branch, which can fuse spatial-frequency-temporal features in a unified framework. Each branch is composed of Linear Embedding and Transformer Encoder, which is used to extract spatial-frequency features and spatial-temporal features. Finally, fusion and classification are performed by the Fusion and Classification layer. Experiments on SEED and SEED-IV datasets demonstrate that Bi-ViTNet outperforms state-of-the-art baselines.

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Pig Face Recognition Based on Trapezoid Normalized Pixel Difference Feature and Trimmed Mean Attention Mechanism

Cited by 16 publications

References 55 publications

Multi-threshold image segmentation using an enhanced fruit fly optimization for COVID-19 X-ray images

Multi-threshold image segmentation using an enhanced fruit fly optimization for COVID-19 X-ray images

VIOLA jones algorithm with capsule graph network for deepfake detection

Bi-Branch Vision Transformer Network for EEG Emotion Recognition

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