Facial Expression Recognition Using Residual Masking Network
Automatic facial expression recognition (FER) has gained much attention due to its applications in human-computer interaction. Among the approaches to improve FER tasks, this paper focuses on deep architecture with the attention mechanism. We propose a novel Masking idea to boost the performance of CNN in facial expression task. It uses a segmentation network to refine feature maps, enabling the network to focus on relevant information to make correct decisions. In experiments, we combine the ubiquitous Deep Residual Network and Unet-like architecture to produce a Residual Masking Network. The proposed method holds state-of-the-art (SOTA) accuracy on the well-known FER2013 and private VEMO datasets. The source code is available at https://github.com/phamquiluan/ResidualMaskingNetwork.
Code (0)
등록된 구현이 없습니다.
Tasks
Facial Expression RecognitionSimilar Papers 제목 키워드 기반
Facial Expression Recognition using Residual Masking Network
Automatic facial expression recognition (FER) has gained much attention due to its applications in human-computer interaction. Among the approaches to improve FER tasks, this paper focuses on deep architecture with the a…
Facial Expression RecognitionFacial Expression Recognition (FER)MERANet: Facial Micro-Expression Recognition using 3D Residual Attention Network
Micro-expression has emerged as a promising modality in affective computing due to its high objectivity in emotion detection. Despite the higher recognition accuracy provided by the deep learning models, there are still …
Micro Expression RecognitionMicro-Expression RecognitionSimFLE: Simple Facial Landmark Encoding for Self-Supervised Facial Expression Recognition in the Wild
One of the key issues in facial expression recognition in the wild (FER-W) is that curating large-scale labeled facial images is challenging due to the inherent complexity and ambiguity of facial images. Therefore, in th…
Face AlignmentFacial Expression RecognitionFacial Expression Recognition (FER)Bounded Residual Gradient Networks (BReG-Net) for Facial Affect Computing
Residual-based neural networks have shown remarkable results in various visual recognition tasks including Facial Expression Recognition (FER). Despite the tremendous efforts have been made to improve the performance of …
Facial Expression RecognitionFacial Expression Recognition (FER)Expression Empowered ResiDen Network for Facial Action Unit Detection
The paper explores the topic of Facial Action Unit (FAU) detection in the wild. In particular, we are interested in answering the following questions: (1) how useful are residual connections across dense blocks for face …
Action Unit DetectionFacial Action Unit DetectionFacial Expression RecognitionFacial Expression Recognition (FER)