Facial Expression Recognition for Learning Status Analysis

Yang, Mau-Tsuen; Cheng, Yiju; Shih, Ya-Chun

doi:10.1007/978-3-642-21619-0_18

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…The learning affective state is the level of a learner's engagement which determined his learning efficiency. Yang, Cheng and Shih [18] supported that the learning affective state is essential to improve students' learning efficiency. The current studies on the online learning affective state are mainly focused on the following two aspects: 1) analysing a learner's knowledge construction process and learning ability based on the learner's online learning logs [19]; 2) analysing a learner's engagement according to the learner's physiological data, such as brain waves, eye movements, and skin electricity [20].…”

Section: A the Affective Statementioning

confidence: 96%

Measuring Students Affective States through Online Learning Logs — An Application of Learning Analytics

Wang¹,

Liu²,

Stein³

et al. 2019

IJIET

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The affective state is determinate to online learning quality. It is related to students' attitude, learning motivation, and learning engagement. Learning affective states consists of engagement, frustration, confusion, and off-task state in this study. Different affective states are associated with different online learning behavior features. Affective states analysis consists of data collecting, data processing, affective states analyzing, evaluating, and intervening. Students' affective states can be analyzed by the affective state detectors. 12,912 online learning operations from an online course are used to construct the affective state detectors and examine the effectiveness. The consistency of the analysis results with the self-reports supported that the model is reliable to analyze students' affective state.Index Terms-Research and application, online affective state; analysis model.

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Section: A the Affective Statementioning

confidence: 96%

Measuring Students Affective States through Online Learning Logs — An Application of Learning Analytics

Wang¹,

Liu²,

Stein³

et al. 2019

IJIET

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show abstract

“…Castrillon [10], Castille [13] and Yang [14] suggested ViolaJones object detection algorithm for coarse face parts (i.e. : eyes, mouth, nose) localization but it did not localize precisely face landmarks and required further, more detailed features detectors.…”

Section: Related Workmentioning

confidence: 99%

Mouth features extraction for emotion classiﬁcation

Robert

Wojciechowski

2016

Annals of Computer Science and Information Systems

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Abstract-Face emotions analysis is one of the fundamental techniques that might be exploited in a natural human-computer interaction process and thus is one of the most studied topics in current computer vision literature. In consequence face features extraction is an indispensable element of the face emotion analysis as it influences decision making performance. The paper concentrates on classification of human poses based on mouth. Mouth features extraction, which next to eye region features becomes one of the most representative face regions in the context of emotions retrieval. Additionally, in the paper, original mouth features extraction method was presented. It is gradient based. Evaluation of the method was performed for a subset of the Yale images database and classification accuracy for single emotion is over 70%.

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“…FER technology has many applications in customer service [2], the automotive industry [3], entertainment [4], and home appliances [5]. There are good examples including: games with different modes based on classifications of the user's facial expression [6], identifying the driver's drowsiness and instructing an appropriate response [7,8], automatically collecting vast amounts of data necessary for the study of human emotional behavior patterns [9], detecting the emotional state of the patient and predicting the situation in need of help [10,11], and establishing an adaptive learning guidance strategy by grasping a student's psychological state using facial expressions and words that are used [12][13][14]. In recent years, interest and research on the development of intelligent home appliances and software that respond to the user's emotional state have been focused on.…”

Section: Introductionmentioning

confidence: 99%

A Robust Facial Expression Recognition Algorithm Based on Multi-Rate Feature Fusion Scheme

Park

Kim

Chilamkurti

2021

Sensors

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In recent years, the importance of catching humans’ emotions grows larger as the artificial intelligence (AI) field is being developed. Facial expression recognition (FER) is a part of understanding the emotion of humans through facial expressions. We proposed a robust multi-depth network that can efficiently classify the facial expression through feeding various and reinforced features. We designed the inputs for the multi-depth network as minimum overlapped frames so as to provide more spatio-temporal information to the designed multi-depth network. To utilize a structure of a multi-depth network, a multirate-based 3D convolutional neural network (CNN) based on a multirate signal processing scheme was suggested. In addition, we made the input images to be normalized adaptively based on the intensity of the given image and reinforced the output features from all depth networks by the self-attention module. Then, we concatenated the reinforced features and classified the expression by a joint fusion classifier. Through the proposed algorithm, for the CK+ database, the result of the proposed scheme showed a comparable accuracy of 96.23%. For the MMI and the GEMEP-FERA databases, it outperformed other state-of-the-art models with accuracies of 96.69% and 99.79%. For the AFEW database, which is known as one in a very wild environment, the proposed algorithm achieved an accuracy of 31.02%.

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Facial Expression Recognition for Learning Status Analysis

Cited by 6 publications

References 11 publications

Measuring Students Affective States through Online Learning Logs — An Application of Learning Analytics

Measuring Students Affective States through Online Learning Logs — An Application of Learning Analytics

Mouth features extraction for emotion classiﬁcation

A Robust Facial Expression Recognition Algorithm Based on Multi-Rate Feature Fusion Scheme

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