Experiments show that the proposed method can improve the classification accuracy by 36% for a non-deep model and 55% for a deep model in occlusion conditions.
EEG signals in emotion recognition absorb special attention owing to their high temporal resolution and their information about what happens in the brain.
In this paper, we propose a new network based on Gated Recurrent Unit (GRU) and two novel post-processing methods for TAL task.
The main challenge in training deep neural networks is the lack of sufficient data to improve the model's generalization and avoid overfitting.
The experimental results on several well-known benchmarks show the outperforming performance of TBO algorithm in finding the global solution.
The proposed regularized Mahalanobis distance metric is used in order to recognize both the involuntary and highly made-up actions at the same time.
The geometry alignment is performed pixel-wise, i. e., every pixel of the face is corresponded to a pixel of the reference face.
This paper proposes a fusion-based gender recognition method which uses facial images as input.
The availability of low-cost range sensors and the development of relatively robust algorithms for the extraction of skeleton joint locations have inspired many researchers to develop human activity recognition methods using the 3-D data.
This method introduces the definition of body states and then every action is modeled as a sequence of these states.