Multi-Level Haar Wavelet Based Facial Expression Recognition using Logistic Regression

Facial expressions play an equally important role as verbal communication and tonal expression. Recognition of facial expression is important in industrial automation, security, medical and many other fields. An image is a very rich and high dimensional data structure, which can result into a considerable computation when processed upon directly. Various feature extraction techniques have been proposed to represent the images efficiently in lower dimension which is understandable by the computer. Configuration and dynamics, both are crucial in the interpretation of facial expressions. This work is based on the configuration of facial texture, it does not account dynamics of muscle change. In this paper, we propose multilevel haar wavelet-based approach, which extracts the features from prominent face regions at two different scales. The approach first segments most informative geometric components such as eye, mouth, eyebrows etc. using the AdaBoost cascade object detector. Haar features of segmented components are extracted. OneVsAll logistic regression model is used for the classification. The performance of the proposed approach is tested on well-known CK, JAFFE and TFEID facial expression datasets, and it achieves 90.48%, 88.57% and 96.84% accuracy for the respective dataset.