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

Background/Objective: Facial expressions play an equally important role as verbal communication and tonal expressions. Recognition of facial expression is important in industrial automation, security, medical and many other fields. Methods/ Statistical Analysis: In this paper, we propose multilevel haar wavelet-based approach, which extracts appearance 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 Viola-Jones cascade object detector. Haar features of segmented components are extracted. One vs. All logistic regression model is used for the classification. Haar features are simple to compute and they can effectively represent the signal in low dimension, yet preserves the energy of the signal. Findings: 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.