Efficient convolutional neural network with multi-kernel enhancement features for real-time facial expression recognition

Li, Minze; Li, Xiaoxia; Sun, Wei; Wang, Xueyuan; Wang, Shunli

doi:10.1007/s11554-021-01088-w

Cited by 13 publications

(4 citation statements)

References 27 publications

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“…In this section, three previous methods conducted by Github et al [16,17], Li et al [38], and Correa et al [39] are compared. Te Github et al method [16,17] is described in the abovementioned section of background.…”

Section: Study Of Previous Workmentioning

confidence: 99%

“…Te Github et al method [16,17] is described in the abovementioned section of background. Li et al [38] proposed a multikernel convolutional block to extract facial expression features. First, this approach was designed through three depth-wise separable convolutional kernels.…”

Section: Study Of Previous Workmentioning

confidence: 99%

“…In this paper, deeply trained models for emotion detection are presented through the use of the FER emotion databases. Te Github et al method [16,17] achieved the best results compared to the Correa et al [39] and Li et al [38] methods. Te simulation has the most prominent values in diferent classes (angry, happy, sad, and neutral).…”

Section: Study Of Previous Workmentioning

confidence: 99%

See 2 more Smart Citations

Designing an Efficient System for Emotion Recognition Using CNN

Ammous,

Chabbouh,

Edhib

et al. 2023

Journal of Electrical and Computer Engineering

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Implementing an efficient system for emotion recognition has recently posed a challenge that has not been fully developed yet. Facial emotion recognition (FER) is an important subject matter in the fields of artificial intelligence (AI) since it exhibits a greater commercial potential. This technique is used to analyse various sentiments and reveal a person’s behavior. It could be related to the mental or physiological state of mind. This paper mainly focuses on a human emotion recognition system through a detected human face. Its accuracy was improved via different data augmentation tools, early stopping, and generative adversarial networks (GANs). Compared to previous methods, experimental results show that the proposed method provides a 0.55% to 35.7% gain performance.

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Section: Study Of Previous Workmentioning

confidence: 99%

Section: Study Of Previous Workmentioning

confidence: 99%

Section: Study Of Previous Workmentioning

confidence: 99%

See 1 more Smart Citation

Designing an Efficient System for Emotion Recognition Using CNN

Ammous,

Chabbouh,

Edhib

et al. 2023

Journal of Electrical and Computer Engineering

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“…VGG16 is a state-of-the-art model which is trained on various classes of objects and outperformed the previous generation of models in the ILSVRC-2012 [ 31 ] and ILSVRC-2013 [ 32 ]. Hence it is chosen to evaluate its accuracy in different scenarios like cropped areas of the faces.…”

Section: Frameworkmentioning

confidence: 99%

A Study of Deep Learning-Based Face Recognition Models for Sibling Identification

Goel

Mehmood

Ugail

2021

Sensors

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Accurate identification of siblings through face recognition is a challenging task. This is predominantly because of the high degree of similarities among the faces of siblings. In this study, we investigate the use of state-of-the-art deep learning face recognition models to evaluate their capacity for discrimination between sibling faces using various similarity indices. The specific models examined for this purpose are FaceNet, VGGFace, VGG16, and VGG19. For each pair of images provided, the embeddings have been calculated using the chosen deep learning model. Five standard similarity measures, namely, cosine similarity, Euclidean distance, structured similarity, Manhattan distance, and Minkowski distance, are used to classify images looking for their identity on the threshold defined for each of the similarity measures. The accuracy, precision, and misclassification rate of each model are calculated using standard confusion matrices. Four different experimental datasets for full-frontal-face, eyes, nose, and forehead of sibling pairs are constructed using publicly available HQf subset of the SiblingDB database. The experimental results show that the accuracy of the chosen deep learning models to distinguish siblings based on the full-frontal-face and cropped face areas vary based on the face area compared. It is observed that VGGFace is best while comparing the full-frontal-face and eyes—the accuracy of classification being with more than 95% in this case. However, its accuracy degrades significantly when the noses are compared, while FaceNet provides the best result for classification based on the nose. Similarly, VGG16 and VGG19 are not the best models for classification using the eyes, but these models provide favorable results when foreheads are compared.

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