2-D canonical correlation analysis based image super-resolution scheme for facial emotion recognition

Ullah, Zia; Qi, Lin; Binu, D.; Rajakumar, B. R.; Ismail, B. Mohammed

doi:10.1007/s11042-022-11922-3

Cited by 6 publications

(2 citation statements)

References 44 publications

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“…Facial emotion recognition is crucial in various domains [53], but low-resolution images can hinder accurate classification [54]. To address this, Ullah et al, have explored the use of 2-D canonical correlation analysis (2-D CCA) for image super-resolution in facial emotion recognition [55]. 2-D CCA finds a correlation between low-resolution and high-resolution image pairs and learns a transformation function.…”

Section: An Analysis Of Prior Research In the Relevant Fieldmentioning

confidence: 99%

The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions

Anbananthan Pillai Munanday,

Norazlianie Sazali,

Arjun Asogan

et al. 2023

ARASET

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The primary objective of this study is to develop a real-time system that can predict the emotional states of an individual who commonly undergoes various experiences. The primary methodology suggested in this research for detecting facial expressions involves the integration of transfer learning techniques that incorporate convolutional neural networks (CNNs), along with a parameterization approach that minimizes the number of parameters. The FER-2013, JAFFE, and CK+ datasets were jointly used to train the CNN architecture for real-time detection, which broadened the range of emotional expressions that may be recognized. The proposed model has the capability to identify various emotions, including but not limited to happiness, fear, surprise, anger, contempt, sadness, and neutrality. Several methods were employed to assess the efficacy of the model's performance in this study. The experimental results indicate that the proposed approach surpasses previous studies in terms of both speed and accuracy.

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Section: An Analysis Of Prior Research In the Relevant Fieldmentioning

confidence: 99%

The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions

Anbananthan Pillai Munanday,

Norazlianie Sazali,

Arjun Asogan

et al. 2023

ARASET

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“…For instance, walking action may be different for every person as each individual walks at his own pace, throwing action may be different and so on [32]. After concatenating the features, intraclass variation of different action classes is analyzed by finding spearman's correlation matrix [51], [52] as shown in equation (7).…”

Section: Intra-class Variationsmentioning

confidence: 99%

goldenAGER: A Personalized Feature Fusion Activity Recognition Model for Elderly

Nagpal

Kumar

Illés³

et al. 2023

IEEE Access

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Physical and mental health are impacted as a person grows old. A Human Activity Recognition (HAR) system, which tracks a person's activity patterns and intervenes in case of an abnormal activity, could help elderly individuals to live independently. However, because of the strong intra class correlation between different activities, it is a challenging task to recognise such activities. Therefore, we proposed a personalized feature fusion algorithm, goldenAGER, which can be used to build as a model for abnormal activity recognition. In the initial stage, it extracts handcrafted HOG features and self-learned VGG-16 features to provide a rich description about the internal information of images. Then, the extracted features are provided as two different inputs to the deep neural network which are finally concatenated to classify the action type. The dataset is collected from the elderly volunteers over the age of 60 in a homogeneous environment consisting 10 classes of activities. The fusion of the features has resulted in 95% accuracy on primary dataset. The performance of the proposed model has also been tested on Microsoft Research (MSR) Action dataset giving accuracy of 93.08%. A comparison of our proposed model with the other existing models is also performed which shows that our model outperformed the existing models.INDEX TERMS Abnormal behaviour recognition, deep neural network, elderly, feature fusion, human activity recognition, HAR.

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An Entropy-Based Computational Classifier for Positive and Negative Emotions in Voice Signals

Herrera-Ortiz

Yáñez-Casas²,

Hernández-Gómez³

et al. 2022

Communications in Computer and Information Science

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2-D canonical correlation analysis based image super-resolution scheme for facial emotion recognition

Cited by 6 publications

References 44 publications

The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions

The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions

goldenAGER: A Personalized Feature Fusion Activity Recognition Model for Elderly

An Entropy-Based Computational Classifier for Positive and Negative Emotions in Voice Signals

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