Evaluation of Facial Action Units in the Wild Considering Nearly Automated Clearing House


Evaluation of Facial Action Units in the Wild Considering Nearly Automated Clearing House – We propose a generic framework for modeling facial action recognition systems, the framework consists of a fully automatic and a fully self-contained, single-model architecture. The goal of this framework is to overcome the limitations in the existing multi-model frameworks, thereby making more realistic applications achievable. A key factor to overcome is to use a differentiable, deep learning-based model which models facial action data well. The framework is also able to learn the underlying representations of facial action recognition. In addition, it generates a high-performance facial action recognition system, which in turn generates a self-contained model for facial action recognition, which can be reused as a baseline for future research in the next stage of the framework. The paper describes how the framework makes use of the information extracted in a large-scale facial action recognition corpus and the ability of the two model networks to learn the feature from the data.

The proposed model-based learning algorithm, Stochastic Gradient Descent (SGD), is a recurrent-learning neural network method for supervised learning of multiple sequential states. In this paper, SGD achieves state-of-the-art performance when used in conjunction with supervised learning, in terms of training samples, and the prediction accuracy of the underlying models. Experimental results suggest that SGD significantly outperforms the state-of-the-art on a test set in sequential classification task, comparing with other state-of-the-art models in many sequential tasks (e.g., unsupervised classification).

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Evaluation of Facial Action Units in the Wild Considering Nearly Automated Clearing House

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  • Learning to Move with Recurrent Neural Networks: A Deep Unsupervised Learning Approach

    Learning Spatial Relations in the Past with Recurrent Neural NetworksThe proposed model-based learning algorithm, Stochastic Gradient Descent (SGD), is a recurrent-learning neural network method for supervised learning of multiple sequential states. In this paper, SGD achieves state-of-the-art performance when used in conjunction with supervised learning, in terms of training samples, and the prediction accuracy of the underlying models. Experimental results suggest that SGD significantly outperforms the state-of-the-art on a test set in sequential classification task, comparing with other state-of-the-art models in many sequential tasks (e.g., unsupervised classification).


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