FERAtt: Facial Expression Recognition With Attention Net

Fernández, Pedro D. Marrero; Peña, Fidel A. Guerrero; Ren, Tsang Ing; Cunha, Alexandre

doi:10.1109/cvprw.2019.00112

Cited by 99 publications

(30 citation statements)

References 30 publications

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“…A visualization of the learned attention maps further more revealed that their models indeed learned to shift their focus onto the unobstructed part of an image in case the face was not clearly visible. Fernandez et al (2019) used the learned attention of a network to remove irrelevant parts of the input data before the final classification step. During their experiments on the CKPlus corpus they found that the attention module improved the overall system classification performance and noise-robustness of the model.…”

Section: Related Workmentioning

confidence: 99%

Relevance-Based Data Masking: A Model-Agnostic Transfer Learning Approach for Facial Expression Recognition

Schiller

Huber

Dietz

et al. 2020

Front. Comput. Sci.

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Deep learning approaches are now a popular choice in the field of automatic emotion recognition (AER) across various modalities. Due to the high costs of manually labeling human emotions however, the amount of available training data is relatively scarce in comparison to other tasks. To facilitate the learning process and reduce the necessary amount of training-data, modern approaches therefore often rely on leveraging knowledge from models that have already been trained on related tasks where data is available abundantly. In this work we introduce a novel approach to transfer learning, which addresses two shortcomings of traditional methods: The (partial) inheritance of the original models structure and the restriction to other neural network models as an input source. To this end we identify the parts in the input that have been relevant for the decision of the model we want to transfer knowledge from, and directly encode those relevant regions in the data on which we train our new model. To validate our approach we performed experiments on well-established datasets for the task of automatic facial expression recognition. The results of those experiments are suggesting that our approach helps to accelerate the learning process.

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Section: Related Workmentioning

confidence: 99%

Relevance-Based Data Masking: A Model-Agnostic Transfer Learning Approach for Facial Expression Recognition

Schiller

Huber

Dietz

et al. 2020

Front. Comput. Sci.

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show abstract

“…In visual emotion recognition, most of the known approaches utilize the emotion-related information extracted through facial features. In this context, D. Pedro et al [11] proposed an end-to-end network for facial emotion recognition using the attention model. However, facial emotion recognition methods are not suitable for IER in non-facial images.…”

Section: Emotion Recognition In Various Modalitiesmentioning

confidence: 99%

Domain Adaptation Based Technique for Image Emotion Recognition Using Image Captions

Kumar

Raman

2021

Communications in Computer and Information Science

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Images are powerful tools for affective content analysis. Image emotion recognition is useful for graphics, gaming, animation, entertainment, and cinematography. In this paper, a technique for recognizing the emotions in images containing facial, non-facial, and nonhuman components has been proposed. The emotion-labeled images are mapped to their corresponding textual captions. Then the captions are used to re-train a text emotion recognition model as the domainadaptation approach. The adapted text emotion recognition model has been used to classify the captions into discrete emotion classes. As image captions have a one-to-one mapping with the images, the emotion labels predicted for the captions have been considered the emotion labels of the images. The suitability of using the image captions for emotion classification has been evaluated using caption-evaluation metrics. The proposed approach serves as an example to address the unavailability of sufficient emotion-labeled image datasets and pre-trained models. It has demonstrated an accuracy of 59.17% for image emotion recognition.

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“…Table 1 shows the major differences of the existing image databases with the number of images, number of subjects, expression distribution, data size, and the released years. However, most of the existing works and datasets [7][8][9][10][11] focus on analyzing adult faces, which ignore how to analyze facial expressions from baby facial images. Although some datasets include children, there are actually very few images of very young children.…”

Section: Introductionmentioning

confidence: 99%

Feature Guided CNN for Baby’s Facial Expression Recognition

Lin

Jiang

2020

Complexity

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State-of-the-art facial expression methods outperform human beings, especially, thanks to the success of convolutional neural networks (CNNs). However, most of the existing works focus mainly on analyzing an adult’s face and ignore the important problems: how can we recognize facial expression from a baby’s face image and how difficult is it? In this paper, we first introduce a new face image database, named BabyExp, which contains 12,000 images from babies younger than two years old, and each image is with one of three facial expressions (i.e., happy, sad, and normal). To the best of our knowledge, the proposed dataset is the first baby face dataset for analyzing a baby’s face image, which is complementary to the existing adult face datasets and can shed some light on exploring baby face analysis. We also propose a feature guided CNN method with a new loss function, called distance loss, to optimize interclass distance. In order to facilitate further research, we provide the benchmark of expression recognition on the BabyExp dataset. Experimental results show that the proposed network achieves the recognition accuracy of 87.90% on BabyExp.

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FERAtt: Facial Expression Recognition With Attention Net

Cited by 99 publications

References 30 publications

Relevance-Based Data Masking: A Model-Agnostic Transfer Learning Approach for Facial Expression Recognition

Relevance-Based Data Masking: A Model-Agnostic Transfer Learning Approach for Facial Expression Recognition

Domain Adaptation Based Technique for Image Emotion Recognition Using Image Captions

Feature Guided CNN for Baby’s Facial Expression Recognition

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