Facial expression recognition with modified local ternary pattern images using convolutional neural network and extreme learning machine
Year: 2024
Type: article
Abstract: Facial emotion recognition is a crucial aspect of human-computer interaction systems, education, health and various other fields. The common challenges faced in FER are variation in light. Based on researches, Local ternary Pattern (LTP) as a feature vector that can handle the variation of light. Modified Local Ternary Pattern (MLTP) is more discriminative to variation of light and less sensitive to noise. MLTP is a conventional feature vector that requires manual processing. In contrast to MLTP, the Convolution Neural Network (CNN) architecture has an automated feature extractor. This paper proposes MLTP as input to the CNN architecture for handling the variation of light and for automatic feature extractor. Then, for classifying Extreme Learning Machine is used to reduce the training time of the CNN classifier. The Karolinska Directed Emotional Faces (KDEF) dataset is used to assess the proposed method and got an accuracy of 86.28%.
Authors: Kailas Nath V D, Jolly B
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4405890075