Facial Expression Recognition Using Visual Saliency and Deep Learning

Mavani, Viraj; Raman, Shanmuganathan; Miyapuram, Krishna Prasad

doi:10.1109/iccvw.2017.327

Cited by 58 publications

(18 citation statements)

References 22 publications

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“…Other researches proposed to automatically learn the key parts for facial expression. For example, [159] employed a deep multi-layer network [160] to detect the saliency map which put intensities on parts demanding visual attention. And [161] applied the neighbor-center difference vector (NCDV) [162] to obtain features with more intrinsic information.…”

Section: Diverse Network Inputmentioning

confidence: 99%

Deep Facial Expression Recognition: A Survey

Deng

2022

IEEE Trans. Affective Comput.

1,092

593

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With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and the recent success of deep learning techniques in various fields, deep neural networks have increasingly been leveraged to learn discriminative representations for automatic FER. Recent deep FER systems generally focus on two important issues: overfitting caused by a lack of sufficient training data and expression-unrelated variations, such as illumination, head pose and identity bias. In this paper, we provide a comprehensive survey on deep FER, including datasets and algorithms that provide insights into these intrinsic problems. First, we introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets. We then describe the standard pipeline of a deep FER system with the related background knowledge and suggestions of applicable implementations for each stage. For the state of the art in deep FER, we review existing novel deep neural networks and related training strategies that are designed for FER based on both static images and dynamic image sequences, and discuss their advantages and limitations. Competitive performances on widely used benchmarks are also summarized in this section. We then extend our survey to additional related issues and application scenarios. Finally, we review the remaining challenges and corresponding opportunities in this field as well as future directions for the design of robust deep FER systems.

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Section: Diverse Network Inputmentioning

confidence: 99%

Deep Facial Expression Recognition: A Survey

Deng

2022

IEEE Trans. Affective Comput.

1,092

593

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“…Study 1 Mavani et al (2017) trained a convolutional neural network to bypass the FACS process, by removing the need for extracting action units. Their study found an overall test accuracy of 95.71% for their model when trained and tested on the Radboud Faces Database (Langner et al, 2010), but fell to 65.39% when attempting to generalise across datasets.…”

Section: Discrete Emotionsmentioning

confidence: 99%

Can Machines Read our Minds?

Burr

Cristianini

2019

Minds & Machines

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We explore the question of whether machines can infer information about our psychological traits or mental states by observing samples of our behaviour gathered from our online activities. Ongoing technical advances across a range of research communities indicate that machines are now able to access this information, but the extent to which this is possible and the consequent implications have not been well explored. We begin by highlighting the urgency of asking this question, and then explore its conceptual underpinnings, in order to help emphasise the relevant issues. To answer the question, we review a large number of empirical studies, in which samples of behaviour are used to automatically infer a range of psychological constructs, including affect and emotions, aptitudes and skills, attitudes and orientations (e.g. values and sexual orientation), personality, and disorders and conditions (e.g. depression and addiction). We also present a general perspective that can bring these disparate studies together and allow us to think clearly about their philosophical and ethical implications, such as issues related to consent, privacy, and the use of persuasive technologies for controlling human behaviour.

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“…They have considered images from RaFD dataset for their experiment. Using visual saliency and deep learning Mavani et al (2017) developed an FER system. They have used CFEE and RaFD dataset.…”

Section: State-of-the-artmentioning

confidence: 99%

“…Deep sparse auto encoders were the basis of their framework. 2015Viola-Jones face detection, AAM, Gabor filter, PCA, ELM JAFFE 94.00 CK+ 95.00 Ghimire and Lee (2014) HOG, ELM ensemble using bagging JAFFE 94.37 CK+ 97.30 Alphonse and Dejey (2016) Facial points, Gabor filter, PCA, ELM JAFFE 91.40 Rao et al (2015) SURF, gentle AdaBoost RaFD 90.64 Mavani et al (2017) Visual saliency,deep learning RaFD 95.70 Zeng et al (2018) AAM, HOG, PCA, deep sparse auto encoders CK+ 95.79…”

Section: State-of-the-artmentioning

confidence: 99%

Fusion of Features and Extreme Learning Machine for Facial Expression Recognition

Islam

Hossain

2019

Journal of Computer Science

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Human emotion is highly correlated to facial expressions. Due to its growing demand in different sectors, an emotion recognition method is proposed through recognizing facial expressions. The input image is preprocessed and then the resulting image is segmented into four facial expression regions following the newly proposed segmentation method. Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are fused to extract the necessary features from the four segmented parts. The dimension of the feature vector is reduced using Principal Component Analysis (PCA). To classify the expressions, Extreme Learning Machine (ELM) is used. For evaluating the performance of the proposed method, three widely used and publicly available facial expression datasets (JAFFE, CK+, RaFD) are used. The proposed method achieved 95.3%, 99.84% and 98.65% accuracy while using images from JAFFE, CK+ and RaFD dataset respectively. Performance of the proposed method on these datasets is compared to other facial expression recognition methods on these datasets to indicate that the proposed method achieves state-of-the-art performance.

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Facial Expression Recognition Using Visual Saliency and Deep Learning

Cited by 58 publications

References 22 publications

Deep Facial Expression Recognition: A Survey

Deep Facial Expression Recognition: A Survey

Can Machines Read our Minds?

Fusion of Features and Extreme Learning Machine for Facial Expression Recognition

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