Multi-Feature Fusion based Non Negative Matrix Factorization: Facial Expression Recognition from Imaging Sensors

Imaging sensors are widely used in HCI applications to capture images for facial expression recognition. The proccess involves extraction of features from captured images and use of machine learning algorithms like K-NN classification to identify the specific expression. We propose here a facial expression recognition system based on non-negative matrix factorization (NMF). As facial parts are more prominent to express a particular facial expression rather than whole faces and NMF does part based analysis, we are interested to analyse how NMF works for Facial expression Recognition. We benchmark our NMF based system on CK+ and JAFFE dataset. We get a significant result. In addition we also propose WAPA and OEPA based NMF for this application. Our proposed WAPA and OEPA is actually two types of fusion method where WAPA counts the all four parts of facial features and we name it as Weighted All Parts Accumulation (WAPA) algorithm. On the otherhand, OEPA counts only the most expressive parts for each expression and we name it as Optimal Expression-specific Parts Accumulation (OEPA). The experiment shows our proposed WAPA and OEPA based NMF outperform the prevalent NMF method.