Gabor Filter Selection and Computational Processing for Emotion Recognition

Abstract

CrowdEmotion produce software to measure a person’s emotions based on analysis of microfacial expressions detected using a webcam. The technology relies on a machine learning algorithm to recognize which features correspond with which emotions; it is trained on a labelled dataset. The features are derived by applying a bank of Gabor filters to a set of frames, determining the Local Binary Pattern (LBP) of each resulting pixel, and then averaging the results over three orthogonal planes (TOP), as outlined in [1]. CrowdEmotion challenged the study group to improve the accuracy, processing speed and cost-efficiency of the tool. In particular they wanted to know if a subset of the bank of Gabor filters was sufficient, and whether the image filtering stage could be implemented on a GPU. A framework for choosing the optimum set of Gabor filters was established, and preliminary testing performed. Different ways of implementing Gabor filters were explored. Some ele- ments of the feature set give little information, thus ways of reducing the dimensionality of this were interrogated. Some steps in the procedure outlined in [1] seemed ad-hoc, in particular when taking a subset of LBPs and choosing a gridding pattern to perform the TOP step. Taking a subset of LBPs was found to be fully justified. Meanwhile choosing a gridding pattern is open to interpretation; we make some suggestions on how this choice might be improved. A short review of alternatives to using a SVM as a classifier is presented.