Multi-angled Face Segmentation and Identification Using Limited Data

This paper introduces a different approach to face segmentation that aims to improve face recognition when given large pose angles and limited training data. Face segmentation is achieved by extracting landmarks which are manipulated in such a way as to normalize unseen data with a classification model. The approach is compared with related systems, followed by further tests that show consistent results across other datasets. Experiments include frontal and non-frontal training images for classification of various face pose angles. The proposed system is a promising contribution, and especially shows the importance of face segmentation. The results are achieved using minimal training data, such that both accurate and practical face recognition systems can be constructed.