Facial Expression Recognition Based on Gabor Multi-orientation Feature Fusion

Facial expression is a key to nonverbal communication, which has been confirmed by many different research projects. A change in intensity or magnitude of even one specific facial expression can cause different interpretations. With the continuous and fast development of computer vision and pattern recognition, facial expression recognition has received significant attention recently due to the wide range of commercial and law enforcement application and the availability of feasible technology during 30 years of research. In this thesis, facial expression recognition is studied by applying several commonly used methods in the whole process. By numerical experiment, we find that our approach with Gabor based transformation for face expression feature extraction, combining the advantages of various algorithms, Gabor wavelet transform and non-negative matrix decomposition of facial expressions are used to obtain features, and CNN is used to classify and apply them to facial expression recognition.