Template matching and machine learning-based robust facial expression recognition system using multi-level Haar wavelet

摘要

Recognition of facial expressions 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. In this paper, we propose Multi-Level Haar wavelet-based approach, which extracts salient features from prominent face regions at two different scales. The approach first segments most informative geometric components such as eye, mouth, etc. using the Adaboost cascade object detector. Segmented components are divided in M × N regions and feature vector is obtained by concatenating local Haar features extracted from each region. Feature vector is projected in Linear Discriminant Analysis space to reduce its size. For classification, we used template matching (Chi-Square and Cosine measure) and machine learning techniques (Logistic Regression and Support Vector Machine). Performance of proposed method is evaluated on various well-known data-sets like CK, Japanese Female Facial Expression, and Taiwanese Facial Expression Image Database. Adaptability of the feature is also tested on in-house Web-Enabled Spontaneous Facial Expression Data-set (WESFED). Comparison with state of the art method shows the superiority of proposed method.