Quantified Facial Expressiveness for Affective Behavior Analytics
The quantified measurement of facial expressiveness is crucial to analyze human affective behavior at scale. Unfortunately, methods for expressiveness quantification at the video frame-level are largely unexplored, unlike the study of discrete expression. In this work, we propose an algorithm that quantifies facial expressiveness using a bounded, continuous expressiveness score using multimodal facial features, such as action units (AUs), landmarks, head pose, and gaze. The proposed algorithm more heavily weights AUs with high intensities and large temporal changes. The proposed algorithm can compute the expressiveness in terms of discrete expression, and can be used to perform tasks including facial behavior tracking and subjectivity quantification in context. Our results on benchmark datasets show the proposed algorithm is effective in terms of capturing temporal changes and expressiveness, measuring subjective differences in context, and extracting useful insight.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Quantified Facial Temporal-Expressiveness Dynamics for Affect Analysis
The quantification of visual affect data (e.g. face images) is essential to build and monitor automated affect modeling systems efficiently. Considering this, this work proposes quantified facial Temporal-expressiveness …
Video-Based Frame-Level Facial Analysis of Affective Behavior on Mobile Devices Using EfficientNets
In this paper, we consider the problem of real-time video-based facial emotion analytics, namely, facial expression recognition, prediction of valence and arousal and detection of action unit points. We propose the novel…
Action Unit DetectionArousal EstimationEmotion RecognitionFacial Expression Recognition+2Facial Affect Recognition in the Wild Using Multi-Task Learning Convolutional Network
This paper presents a neural network based method Multi-Task Affect Net(MTANet) submitted to the Affective Behavior Analysis in-the-Wild Challenge in FG2020. This method is a multi-task network and based on SE-ResNet mod…
Emotion ClassificationMulti-Task LearningFrame-level Prediction of Facial Expressions, Valence, Arousal and Action Units for Mobile Devices
In this paper, we consider the problem of real-time video-based facial emotion analytics, namely, facial expression recognition, prediction of valence and arousal and detection of action unit points. We propose the novel…
Arousal EstimationEmotion RecognitionFacial Expression RecognitionFacial Expression Recognition (FER)Causal affect prediction model using a facial image sequence
Among human affective behavior research, facial expression recognition research is improving in performance along with the development of deep learning. However, for improved performance, not only past images but also fu…
Causal InferenceFacial Expression RecognitionFacial Expression Recognition (FER)Prediction