Expression-Guided Deep Joint Learning for Facial Expression Recognition.

ABSTRACT: In recent years, convolutional neural networks (CNNs) have played a dominant role in facial expression recognition. While CNN-based methods have achieved remarkable success, they are notorious for having an excessive number of parameters, and they rely on a large amount of manually annotated data. To address this challenge, we expand the number of training samples by learning expressions from a face recognition dataset to reduce the impact of a small number of samples on the network training. In the proposed deep joint learning framework, the deep features of the face recognition dataset are clustered, and simultaneously, the parameters of an efficient CNN are learned, thereby marking the data for network training automatically and efficiently. Specifically, first, we develop a new efficie

SUBMITTER: Fang B 

PROVIDER: S-EPMC10457757 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature