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Abstract
Augmented Reality devices (AR), virtual reality devices (VR), are changing our lives and it is critical to provide intelligent interaction and improve the user's intelligent interactive experience. When artificial intelligence is introduced into Intelligent interaction emotion classification or semantic segmentation and other tasks, it requires professional knowledge to manually label images sample. To address the problem of scarcity of labeled data in emotion classification, an improved classification method based on semi-supervised generative adversarial networks (GAN) is proposed in this paper. Firstly, the output layer of the traditional unsupervised GAN is replaced with Softmax layer to obtain the semi-supervised GAN. Secondly, additional labels are defined for generated samples to guiding the training process. Finally, we employ a semi-supervised training strategy to optimize the parameters of GAN and use the trained network to process videos. Experiments on existing public datasets show that our method has a certain improvement in compared with the classic methods based on deep learning, and has a higher recognition efficiency, which is more suitable for dimension emotion recognition of large-scale data.
Highlights
Smart glasses such as Google Glass, HoloLens, and some head-mounted smart devices such as Augmented Reality devices (AR), virtual reality devices (VR), are changing our lives [1]–[3]
To address the problem of scarcity of labeled data in emotion classification, an improved classification method based on semi-supervised generative adversarial networks (GAN) is proposed in this paper
We employ a semi-supervised training strategy to optimize the parameters of GAN and use the trained network to process videos
Summary
Smart glasses such as Google Glass, HoloLens, and some head-mounted smart devices such as Augmented Reality devices (AR), virtual reality devices (VR), are changing our lives [1]–[3]. With adversarial training literature [13] can promote the feature fusion of emotion state and images, and make network learn a more effective common subspace. To address the problem of scarcity of labeled data in emotion classification, an improved classification method based on semi-supervised generative adversarial networks (GAN) is proposed in this paper. The model adjusts the network parameters by the training method combining supervised loss and unsupervised loss It can improve the learning ability of GAN by feature matching, and improve the accuracy of discriminant image classification in the process of confrontation. There are three main steps in our proposed emotion interaction recognition
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