arXiv:2010.01301
공식 코드 · Papers with Code
Deep Convolutional Neural Network Based Facial Expression Recognition in the Wild
원문 초록
This paper describes the proposed methodology, data used and the results of our participation in the ChallengeTrack 2 (Expr Challenge Track) of the Affective Behavior Analysis in-the-wild (ABAW) Competition 2020. In this competition, we have used a proposed deep convolutional neural network (CNN) model to perform automatic facial expression recognition (AFER) on the given dataset. Our proposed model has achieved an accuracy of 50.77% and an F1 score of 29.16% on the validation set.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Data
V-NAW: Video-based Noise-aware Adaptive Weighting for Facial Expression Recognition
Biased Heritage: How Datasets Shape Models in Facial Expression Recognition
Navigating Label Ambiguity for Facial Expression Recognition in the Wild
Unimodal and Multimodal Static Facial Expression Recognition for Virtual Reality Users with EmoHeVRDB
QCS: Feature Refining from Quadruplet Cross Similarity for Facial Expression Recognition
토론
이 논문과 연결된 포럼 글이 아직 없습니다.