Probabilistic neural network and invariant moments for men face shape classification

Abstract

Face shape classification is useful for grooming personalities, such as the selection of haircut, the selection of facial makeup, the selection of glasses frames or even the selection of appropriate shirts. The face shape in men is divided into six forms, namely: oval, round, diamond, rectangle, triangle and square. Facial shape determination has been introduced by many beauty experts, but for society, in general, is still a little difficult to classify it because the form of each face is almost the same and manual measurement requires a long process. That’s why it needs a method to classify face shape quickly and precisely. A proposed method in this research is Probability Neural Network and Invariant Moments. Men face images are used as input for image processing. The stages before classification are image pre-processing (Gray scaling, Scaling, and Gabor Filter). Then feature extraction using Invariant Moments. The final step is classification using Probability Neural Network. After testing is done to 90 data training and 30 data testing, it was concluded that the proposed method has the capability to classify men face shape with accuracy 80%.