Facial expression recognition (FER) becomes more challenging in the wild due to unconstrained conditions, such as the different illumination, pose changes, and occlusion of the face. Current FER methods deploy the attention mechanism in deep neural networks to improve the performance. However, these models only capture the limited attention features and relationships. Thus this paper proposes a novel FER framework called multi-relations aware network (MRAN), which can focus on global and local attention features and learn the multi-level relationships among local regions, between global-local features and among different samples, to obtain efficient emotional features. Specifically, our method first imposes the spatial attention on both the whole face and local regions to simultaneously learn the global and local salient features. After that, a region relation transformer is deployed to capture the internal structure among local facial regions, and a global-local relation transformer is designed to learn the fusion relations between global features and local features for different facial expressions. Subsequently, a sample relation transformer is deployed to focus on intrinsic similarity relationship among training samples, which promotes invariant feature learning for each expression. Finally, a joint optimization strategy is designed to efficiently optimize the model. The conducted experimental results on in-the-wild databases show that our method obtains the superior performance compared to some state-of-the-art models.

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.

The tongue image provides important physical information of humans. It is of great importance for diagnoses and treatments in clinical medicine. Herbal prescriptions are simple, noninvasive, and have low side effects. Thus, they are widely applied in China. Studies on the automatic construction technology of herbal prescriptions based on tongue images have great significance for deep learning to explore the relevance of tongue images for herbal prescriptions, it can be applied to healthcare services in mobile medical systems. In order to adapt to the tongue image in a variety of photographic environments and construct herbal prescriptions, a neural network framework for prescription construction is designed. It includes single/double convolution channels and fully connected layers. Furthermore, it proposes the auxiliary therapy topic loss mechanism to model the therapy of Chinese doctors and alleviate the interference of sparse output labels on the diversity of results. The experiment use the real-world tongue images and the corresponding prescriptions and the results can generate prescriptions that are close to the real samples, which verifies the feasibility of the proposed method for the automatic construction of herbal prescriptions from tongue images. Also, it provides a reference for automatic herbal prescription construction from more physical information.