A Photo-Realistic Generator of Most Expressive and Discriminant Changes in 2D Face Images

Abstract

This work describes a photo-realistic generator that creates semi-automatically face images of unseen subjects. Unlike previously described methods for generating face imagery, the approach described herein incorporates texture and shape information in a single computational framework based on high dimensional encoding of variance and discriminant information from sample groups. The method produces realistic, frontal pose, images with minimum manual intervention. We believe that the work presented describes a useful tool for face perception applications where privacy-preserving analysis might be an issue and the goal is not the recognition of the face itself, but rather its characteristics like gender, age or race, commonly explored in social and forensic contexts.