Edge-based facial feature extraction using Gabor wavelet and convolution filters

Feature extraction is a crucial step for many systems of face detection and facial expression recognition. In this paper, we present edge-based feature extraction for recognizing six different expressions, which are angry, fear, happy, neutral, sadness and surprise. Edge detection is performed by using Gabor wavelet and convolution filters. In this paper we propose two convolution kernels that are specific for the edge detection of facial components in two orientations. In this study, Principal Component Analysis (PCA) is used to reduce the features dimension. To validate the performance of our proposed feature extraction, the generated features are classified using Support Vector Machine. The experimental results demonstrated that the proposed feature extraction method could generate significant facial features and these features are able to be classified into each expression.