A Real Time Facial Expression Classification System Using Local Binary Patterns

TL;DR

This paper presents a real-time facial expression classification system that combines Haar face detection, Local Binary Patterns for feature extraction, and PCA for classification, enabling efficient emotion recognition adaptable to individual differences.

Contribution

The paper introduces a real-time facial expression classification algorithm using LBP and PCA, with a customizable approach for individual expression variations.

Findings

Achieved real-time performance with low computational complexity

Successfully classified six basic emotions from grayscale face images

Demonstrated adaptability to individual differences in expressions

Abstract

Facial expression analysis is one of the popular fields of research in human computer interaction (HCI). It has several applications in next generation user interfaces, human emotion analysis, behavior and cognitive modeling. In this paper, a facial expression classification algorithm is proposed which uses Haar classifier for face detection purpose, Local Binary Patterns (LBP) histogram of different block sizes of a face image as feature vectors and classifies various facial expressions using Principal Component Analysis (PCA). The algorithm is implemented in real time for expression classification since the computational complexity of the algorithm is small. A customizable approach is proposed for facial expression analysis, since the various expressions and intensity of expressions vary from person to person. The system uses grayscale frontal face images of a person to classify six basic emotions namely happiness, sadness, disgust, fear, surprise and anger.