FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition
Hui Ding,Shaohua Kevin Zhou,Rama Chellappa +2 more
- pp 118-126
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TLDR
FaceNet2ExpNet as mentioned in this paper proposes a new distribution function to model the high-level neurons of the expression network, which achieves better results than state-of-the-art methods.Abstract:
Relatively small data sets available for expression recognition research make the training of deep networks very challenging. Although fine-tuning can partially alleviate the issue, the performance is still below acceptable levels as the deep features probably contain redundant information from the pretrained domain. In this paper, we present FaceNet2ExpNet, a novel idea to train an expression recognition network based on static images. We first propose a new distribution function to model the high-level neurons of the expression network. Based on this, a two-stage training algorithm is carefully designed. In the pre-training stage, we train the convolutional layers of the expression net, regularized by the face net; In the refining stage, we append fully-connected layers to the pre-trained convolutional layers and train the whole network jointly. Visualization results show that the model trained with our method captures improved high-level expression semantics. Evaluations on four public expression databases, CK+, Oulu- CASIA, TFD, and SFEW demonstrate that our method achieves better results than state-of-the-art.read more
Citations
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Proceedings ArticleDOI
DPCNet: Dual Path Multi-Excitation Collaborative Network for Facial Expression Representation Learning in Videos
TL;DR: A Dual Path multi-excitation Collaborative Network (DPCNet) is proposed to learn the critical information for facial expression representation from fewer keyframes in videos and designs a multi-frame regularization loss to enforce the representation of multiple frames in the dual view to be semantically coherent.
Journal ArticleDOI
Self-Difference Convolutional Neural Network for Facial Expression Recognition
TL;DR: Li et al. as discussed by the authors proposed a self-difference convolutional network (SD-CNN) to address the intra-class variation issue in facial expression recognition, which achieved state-of-the-art performance with accuracies of 997% on CK+ and 913% on Oulu-CASIA.
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Identity-Aware Facial Expression Recognition Via Deep Metric Learning Based on Synthesized Images
TL;DR: In this article , a novel identity-aware method is proposed to solve the challenging person-dependent facial expression recognition task based on deep metric learning and facial image synthesis techniques, and a StarGAN is incorporated to synthesize facial images depicting different but complete basic emotions for each identity to augment the training data.
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The current challenges of automatic recognition of facial expressions: A systematic review
TL;DR: A systematic review of the literature according to the guidelines of the PRISMA method highlights the strengths, limitations and main directions for future research in this field of automated facial expression recognition.
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Apathy Classification by Exploiting Task Relatedness
TL;DR: In this article, a multi-task learning (MTL) framework for apathy classification based on facial analysis, entailed both emotion and facial movements, was proposed, which leverages information from other auxiliary tasks (e.g., clinical scores) which might be closely or distantly related to the main task of apathy diagnosis.
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