A Preprocessing Approach to Improve the Performance of Inception v3-based Face Shape Classification

Face shape classification is considered one of the trending topics in the artificial intelligence research field. Face shape classification can be employed in many broad-scoped projects, such as hairstyle recommendation systems in the beauty and fashion industry. In this paper, the inception v3 model was employed to reach the highest possible performance for classifying the different face shapes. The model was re-trained after applying a proposed sequence of preprocessing techniques, including image straightening, cropping, resizing, and normalization. The model was re-trained on different data sizes of females' images. Comparing the proposed model to the other state-of-art previous work showed an outperforming testing accuracy of 94.3%.