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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Journal ArticleDOI
Emotion Recognition for Cognitive Edge Computing Using Deep Learning
TL;DR: Experimental results show that the proposed emotion recognition system from facial images based on edge computing is energy efficient, has less learnable parameters, and good recognition accuracy.
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Facial Expression Recognition with Neighborhood-Aware Edge Directional Pattern (NEDP)
TL;DR: A novel local descriptor named Neighborhood-aware Edge Directional Pattern (NEDP) is proposed, which examines the gradients at the target (center) pixel as well as its neighboring pixels to explore a wider neighborhood for the consistency of the feature in spite of the presence of subtle distortion and noise in local region.
Book ChapterDOI
Deep Multi-Task Learning to Recognise Subtle Facial Expressions of Mental States
Guosheng Hu,Li Liu,Yang Yuan,Zehao Yu,Yang Hua,Zhihong Zhang,Fumin Shen,Ling Shao,Timothy M. Hospedales,Neil Robertson,Yongxin Yang +10 more
TL;DR: This work addresses subtle expression recognition through convolutional neural networks (CNNs) by developing multi-task learning (MTL) methods to effectively leverage a side task: facial landmark detection and achieves very competitive performance on Oulu-Casia NIR&Vis and CK+ databases via transfer learning.
Proceedings ArticleDOI
Deep Disturbance-Disentangled Learning for Facial Expression Recognition
TL;DR: This paper proposes a novel Deep Disturbance-disentangled Learning (DDL) method for FER that is capable of simultaneously and explicitly disentangling multiple disturbing factors by taking advantage of multi-task learning and adversarial transfer learning.
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TL;DR: An effective pre-processing step has been added for WSI for better predictability of image parts and faster training, and HIC-net has more successful results than other state-of-art CNN algorithms with AUC score of 97.7%.
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