Automatic Recognition of Facial Displays of Unfelt Emotions

Kulkarni, Kaustubh; Corneanu, Ciprian A.; Ofodile, Ikechukwu; Escalera, Sérgio; Baró, Xavier; Hyniewska, Sylwia; Allïk, Jüri; Anbarjafari, Gholamreza

doi:10.1109/taffc.2018.2874996

Cited by 37 publications

(26 citation statements)

References 124 publications

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“…Furthermore, deep learning techniques have been thoroughly applied by the participants of these two challenges (e.g., [240], [241], [242]). Additional related real-world applications, such as the Real-time FER App for smartphones [243], [244], Eyemotion (FER using eye-tracking cameras) [245], privacy-preserving mobile analytics [246], Unfelt emotions [247] and Depression recognition [248], have also been developed.…”

Section: Other Special Issuesmentioning

confidence: 99%

Deep Facial Expression Recognition: A Survey

Deng

2022

IEEE Trans. Affective Comput.

1,021

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: Other Special Issuesmentioning

confidence: 99%

Deep Facial Expression Recognition: A Survey

Deng

2022

IEEE Trans. Affective Comput.

1,021

593

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“…Facial expression (FE) identification system is fast becoming a well-known feature for many distinct reasons in 'applications' and websites [4,5]. In addition, the individuality of the facial feature is very efficient in the biometric credentials that automatically identifies a individual person from a digital image or a video image.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised facial expression detection using genetic algorithm

Dembani¹,

Wang²,

Sun³

et al. 2020

NNW

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Interpersonal communication can be done by understanding the clues of facial expressions. As its importance increase in behavior and clinical studies, so automatic detection of facial expressions is an open research area for the last few decades. Efforts of expression detection by a human being are easy and effective but the machine needs some more understanding. This paper proposes a face expression clustering using a genetic algorithm. Image get convert into binary format for finding the related cluster selection in different phases of genetic algorithm. Proposed work has utilized a modified teacher learning-based optimization algorithm where the population gets updated in each phase to get the best representative features. A real dataset of facial expression was used in this work. A comparison of the proposed model was done with existing models on different evaluation parameters. It was obtained that the proposed work has improved precision, recall, the accuracy of facial expression identification without any training.

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“…On the basis of external behaviour one can easily determine the internal state of the interlocutor. For example, burst of laughter generally signals amusement, frowning signals nervousness or irritation, crying is closely related to sadness and weakness [ 5 , 6 , 7 ]. Mehrabian formulated the principle 7-38-55, according to which the percentage distribution of the message is as follows: 7% verbal signals and words, 38% strength, height, and rhythm and 55% body movements and facial expressions [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition from Skeletal Movements

Sapiński

Kamińska

Pelikant

et al. 2019

Entropy

Self Cite

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Automatic emotion recognition has become an important trend in many artificial intelligence (AI) based applications and has been widely explored in recent years. Most research in the area of automated emotion recognition is based on facial expressions or speech signals. Although the influence of the emotional state on body movements is undeniable, this source of expression is still underestimated in automatic analysis. In this paper, we propose a novel method to recognise seven basic emotional states—namely, happy, sad, surprise, fear, anger, disgust and neutral—utilising body movement. We analyse motion capture data under seven basic emotional states recorded by professional actor/actresses using Microsoft Kinect v2 sensor. We propose a new representation of affective movements, based on sequences of body joints. The proposed algorithm creates a sequential model of affective movement based on low level features inferred from the spacial location and the orientation of joints within the tracked skeleton. In the experimental results, different deep neural networks were employed and compared to recognise the emotional state of the acquired motion sequences. The experimental results conducted in this work show the feasibility of automatic emotion recognition from sequences of body gestures, which can serve as an additional source of information in multimodal emotion recognition.

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Automatic Recognition of Facial Displays of Unfelt Emotions

Cited by 37 publications

References 124 publications

Deep Facial Expression Recognition: A Survey

Deep Facial Expression Recognition: A Survey

Unsupervised facial expression detection using genetic algorithm

Emotion Recognition from Skeletal Movements

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