Emotion Recognition Using Representative Geometric Feature Mask Based on CNN

Emotion recognition is a growing area of facial recognition, to detect the basic emotion state of a person and then operate further analysis. For practical applications, high speed and accuracy are required as an efficient and precise system. To this end, the paper proposes an effective emotion recognition system using a representative geometric feature mask for feature extraction and a CNN model for classification. Compared with traditional emotion recognition systems, which usually extract facial key features and then convert them into mathematical information variables by equations, the system implemented in this paper extracts necessary features in facial expression through landmarks, and operates a further extraction by a transformation that converts features into a pure geometric feature mask to represent a simplified human face. Then, the mask that can be used to express the human facial emotion with fewer noise features, is input into a deep learning training CNN (Convolutional Neural Network) model. The improvement of this work is that the system combines pure geometric method to extract facial features with CNN algorithm properties in image processing, where local connectivity and shared parameter properties were fully used in further geometric feature extraction. Finally, the system achieves high accuracy and low time costs with KDEF (Karolinska Directed Emotional Faces) and CK+ (Cohn-Kanade AU-Coded Expression Database).