Proceedings ArticleDOI
Adaptive Visual Feedback Generation for Facial Expression Improvement with Multi-task Deep Neural Networks
Takuhiro Kaneko,Kaoru Hiramatsu,Kunio Kashino +2 more
- pp 327-331
TLDR
This work proposes a learning-based method that implicitly learns the rules from training data consisting of input images, key-point annotations, and state annotations that do not require professional knowledge in feedback and applies a novel propagation method for extracting feedback information from the network.Abstract:
While many studies in computer vision and pattern recognition have been actively conducted to recognize people's current states, few studies have tackled the problem of generating feedback on how people can improve their states, although there are many real-world applications such as in sports, education, and health care. In particular, it has been challenging to develop such a system that can adaptively generate feedback for real-world situations, namely various input and target states, since it requires formulating various rules of feedback to do so. We propose a learning-based method to solve this problem. If we can obtain a large amount of feedback annotations, it is possible to explicitly learn the rules, but it is difficult to do so due to the subjective nature of the task. To mitigate this problem, our method implicitly learns the rules from training data consisting of input images, key-point annotations, and state annotations that do not require professional knowledge in feedback. Given such training data, we first learn a multi-task deep neural network with state recognition and key-point localization. Then, we apply a novel propagation method for extracting feedback information from the network. We evaluated our method in a facial expression improvement task using real-world data and clarified its characteristics and effectiveness.read more
Citations
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Journal ArticleDOI
Deep Facial Expression Recognition: A Survey
Shan Li,Weihong Deng +1 more
TL;DR: A comprehensive survey on deep facial expression recognition (FER) can be found in this article, including datasets and algorithms that provide insights into the intrinsic problems of deep FER, including overfitting caused by lack of sufficient training data and expression-unrelated variations, such as illumination, head pose and identity bias.
Journal ArticleDOI
Deep Facial Expression Recognition: A Survey
TL;DR: A comprehensive review of deep facial expression recognition (FER) including datasets and algorithms that provide insights into these intrinsic problems can be found in this article , where the authors introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets.
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OmniArt: Multi-task Deep Learning for Artistic Data Analysis.
Gjorgji Strezoski,Marcel Worring +1 more
TL;DR: An efficient and accurate method for multi-task learning with a shared representation applied in the artistic domain and a challenge like nature to the new aggregated data set with almost half a million samples and structured meta-data to encourage further research and societal engagement.
Proceedings ArticleDOI
Exploring Joint Neural Model for Sentence Level Discourse Parsing and Sentiment Analysis
TL;DR: This work design and compare two Neural Based models for jointly learning both Discourse Parsing and Sentiment Analysis and sees improvements in the prediction of the set of contrastive relations.
Proceedings Article
Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition
TL;DR: A constraint optimization method is proposed to encode the generic knowledge on expression-AUs probabilistic dependencies into a Bayesian Network (BN), then integrated into a deep learning framework as a weak supervision for an AU detection model.
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