A fusion convolution neural network-local binary pattern histogram algorithm for emotion recognition in human

Abstrak

This paper proposes a fusion of algorithms namely convolution neural networks (CNN) and local binary pattern histogram (LBPH) techniques to comprehend the emotions in humans for greyscale images. In this work, the combined advantages of CNN for its ability to extract features, suitability for image processing and LBPH algorithm to identify the emotions of the human images are included. Though there are enhanced fused algorithms with CNN for image processing, the combination of LBPH with CNN is precise and simple in design. In this work, the secondary data sample is used to recognize the human emotions. The secondary data set consists of 160 samples with emotions of happy, anger, sad, and surprise is considered for making decisions. In comparison, the accuracy of the proposed method is high compared to the other algorithms.