Variation of deep features analysis for facial expression recognition system

In this paper, a unique facial expression recognition system has been proposed. The objective of this paper is to identify the type of human facial expression and to improve the performance by incorporating different variant patterns present in facial images. In literature, the use of different patterns of the human face has not yet been employed in its full swing. In our proposed method, this gap has been overcome by fusing a data augmentation in the preprocessing step and an optimized weight in the deep CNN model at the feature extraction step. To make this proposed model noise-free, we focus on the feature extraction method rather than designing a complex model to decompose a set of feature vectors into expression-specific feature vectors. Here, to analyze the performance of FER system, we use cultural difference datasets in both laboratory controlled and wild environments. To develop a precise facial expression recognition system, we propose an invariant deep convolutional neural network (DCNN) model that learns from all image variants to improve performance. Our model also induces a new pipeline strategy to correlate a preprocessing and feature extraction step in an optimized way. Experiments on laboratory and wild-controlled datasets show that our FER system attains a more significant performance than other state-of-the-art models due to its ability to utilize all the different variant patterns present in the facial image.

Automated summary

This paper proposes a unique facial expression recognition system that improves performance by incorporating different variant patterns in facial images. The proposed model utilizes a deep convolutional neural network and incorporates cultural difference datasets. Experimental results show that the FER system achieves more significant performance compared to other state-of-the-art models.