A Real Time Facial Expression Classification System Using Local Binary Patterns
This work addresses the problem of real-time emotion analysis for human-computer interaction applications, but it is incremental as it combines existing methods like LBP and PCA without introducing major innovations.
The paper tackles real-time facial expression classification by proposing an algorithm that uses Haar classifier for face detection, Local Binary Patterns (LBP) histograms as features, and Principal Component Analysis (PCA) for classification, achieving implementation with low computational complexity.
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.