Parameter optimization of histogram-based local descriptors for facial expression recognition

Abstract

An important task in automatic facial expression recognition (FER) is to describe facial image features effectively and efficiently. Facial expression descriptors must be robust to variable scales, illumination changes, face view, and noise. This article studies the application of spatially modified local descriptors to extract robust features for facial expressions recognition. The experiments are carried out in two phases: firstly, we motivate the need for face registration by comparing the extraction of features from registered and non-registered faces, and secondly, four local descriptors (Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Compound Local Binary Patterns (CLBP), and Weber’s Local Descriptor (WLD)) are optimized by finding the best parameter values for their extraction. Our study reveals that face registration is an important step that can improve the recognition rate of FER systems. We also highlight that a suitable parameter selection can increase the performance of existing local descriptors as compared with state-of-the-art approaches.

Keywords

• Image registration
• Feature extraction
• Facial expression recognition
• Classification
• Local descriptors
• Parameter optimization