Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation: Databases

Lüsi, Iiris; Jacques, Julio C. S.; Gorbova, Jelena; Baró, Xavier; Escalera, Sérgio; Demirel, Hasan; Allïk, Jüri; Özçinar, Çağrı; Anbarjafari, Gholamreza

doi:10.1109/fg.2017.102

Cited by 33 publications

(15 citation statements)

References 21 publications

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“…Several novel issues have been approached on the basis of the prototypical expression categories: dominant and complementary emotion recognition challenge [238] and the Real versus Fake expressed emotions challenge [239]. Furthermore, deep learning techniques have been thoroughly applied by the participants of these two challenges (e.g., [240], [241], [242]).…”

Section: Other Special Issuesmentioning

confidence: 99%

Deep Facial Expression Recognition: A Survey

Deng

2022

IEEE Trans. Affective Comput.

1,082

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.

1,082

593

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“…For the Head Pose Challenge [11] organized at the International Conference on Automatic Face and Gesture Recognition (FG 2017), the SASE data has been divided in three sets: Training (comprising 28 subjects with a total of ∼ 17K images), Validation (12 subjects, ∼ 7K images) and Test (10 subjects, ∼ 6K images). Only the Training data was made available to challenge participants in order to investigate the performance of their algorithms prior to the final evaluation phase.…”

Section: Methodsmentioning

confidence: 99%

“…This setup has been specified in the FG2017 Head-Pose Estimation Challenge [11]. In contrast to most existing approaches, we base our system in the detection of 3D facial landmarks, whose positions are later used to derive geometry-and patch-based pose estimators.…”

Section: Introductionmentioning

confidence: 99%

Head Pose Estimation Based on 3-D Facial Landmarks Localization and Regression

Derkach

Ruiz

Sukno

2017

2017 12th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2017)

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Abstract-In this paper we present a system that is able to estimate head pose using only depth information from consumer RGB-D cameras such as Kinect 2. In contrast to most approaches addressing this problem, we do not rely on tracking and produce pose estimation in terms of pitch, yaw and roll angles using single depth frames as input. Our system combines three different methods for pose estimation: two of them are based on state-ofthe-art landmark detection and the third one is a dictionarybased approach that is able to work in especially challenging scans where landmarks or mesh correspondences are too difficult to obtain. We evaluated our system on the SASE database, which consists of ∼30K frames from 50 subjects. We obtained average pose estimation errors between 5 and 8 degrees per angle, achieving the best performance in the FG2017 Head Pose Estimation Challenge. Full code of the developed system is available on-line.

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“…Although both tasks have been studied extensively in the past (see, e.g. [1,2,3,4,5]), we consider two settings of practical importance that have not been studied deeply. On the one hand, we organize a challenge on large scale multimodal gesture recognition.…”

Section: Introductionmentioning

confidence: 99%

Results and Analysis of ChaLearn LAP Multi-modal Isolated and Continuous Gesture Recognition, and Real Versus Fake Expressed Emotions Challenges

Wan

Escalera²,

Anbarjafari³

et al. 2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

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We analyze the results of the 2017 ChaLearn Looking at People Challenge at ICCV. The challenge comprised three tracks: (1) large-scale isolated (2) continuous gesture recognition, and (3) real versus fake expressed emotions tracks. It is the second round for both gesture recognition challenges, which were held first in the context of the ICPR 2016 workshop on "multimedia challenges beyond visual analysis". In this second round, more participants joined the competitions, and the performances considerably improved compared to the first round. Particularly, the best recognition accuracy of isolated gesture recognition has improved from 56.90% to 67.71% in the IsoGD test set, and Mean Jaccard Index (MJI) of continuous gesture recognition has improved from 0.2869 to 0.6103 in the ConGD test set. The third track is the first challenge on real versus fake expressed emotion classification, including six emotion categories, for which a novel database was introduced. The first place was shared between two teams who achieved 67.70% averaged recognition rate on the test set. The data of the three tracks, the participants' code and method descriptions are publicly available to allow researchers to keep making progress in the field.

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Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation: Databases

Cited by 33 publications

References 21 publications

Deep Facial Expression Recognition: A Survey

Deep Facial Expression Recognition: A Survey

Head Pose Estimation Based on 3-D Facial Landmarks Localization and Regression

Results and Analysis of ChaLearn LAP Multi-modal Isolated and Continuous Gesture Recognition, and Real Versus Fake Expressed Emotions Challenges

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