Facial expression recognition on real world face images using intelligent techniques: A survey

Most of the research in facial expression recognition system has been done on the datasets which are acquired in predetermined lab environment. Acquiring real world data is crucial for the benchmarking of algorithms because facial expression recognition system will eventually have to work on the data from real world in an uncertain environment. In the recent years, the local descriptor-based facial expression recognition has been shown to be more robust to pose changes, misalignment and illumination effects. Local Binary Pattern (LBP) and Weber Local Descriptor (WLD) are commonly used local descriptors used to effectively represent local micro texture information of the face. Classification of facial expression images became more challenging due to different norms and gestures used among different races and ethnic groups and the change between different expression classes is very subtle. It necessitates storing not only micro but also macro texture information in case of real world face images to acquire better accuracy results. Traditional local descriptors are not capable to extract the macro texture information. The purpose of the proposed work is to present a novel framework to recognize facial expressions with high accuracy rate. The proposed framework intends to be time and memory efficient, and unlike LBP and WLD it would not only store micro but also macro texture information. Performance of the proposed technique will be measured using both synthetic (i.e. lab-based) and real world face images datasets.