Feature selection for facial emotion recognition using late hill-climbing based memetic algorithm

Facial Emotion Recognition (FER) is an important research domain which allows us to provide a better interactive environment between humans and computers. Some standard and popular features extracted from facial expression images include Uniform Local Binary Pattern (uLBP), Horizontal-Vertical Neighborhood Local Binary Pattern (hvnLBP), Gabor filters, Histogram of Oriented Gradients (HOG) and Pyramidal HOG (PHOG). However, these feature vectors may contain some features that are irrelevant or redundant in nature, thereby increasing the overall computational time as well as recognition error of a classification system. To counter this problem, we have proposed a new feature selection (FS) algorithm based on Late Hill Climbing and Memetic Algorithm (MA). A novel local search technique called Late Acceptance Hill Climbing through Redundancy and Relevancy (LAHCRR) has been used in this regard. It combines the concepts of Local Hill-Climbing and minimal-Redundancy Maximal-Relevance (mRMR) to form a more effective local search mechanism in MA. The algorithm is then evaluated on the said feature vectors extracted from the facial images of two popular FER datasets, namely RaFD and JAFFE. LAHCRR is used as local search in MA to form Late Hill Climbing based Memetic Algorithm (LHCMA). LHCMA is compared with state-of-the-art methods. The experimental outcomes show that the proposed FS algorithm reduces the feature dimension to a significant amount as well as increases the recognition accuracy as compared to other methods.