Digital Face Classification and Beautification Based on Support Vector Regression

• Main Authors: Heng-Wen Liu, 劉�皕�
• Other Authors: 歐陽明
• Format: Others
• Language: zh-TW
• Online Access: http://ndltd.ncl.edu.tw/handle/50746768091592480686

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 96 === It’s not a secret that we can make people in the photo more beautiful and attractive by using application software for modifying procedures. How to make photos looks more attractive is even an artistic issue for professional photographers and commercial designers, however, such modifying procedure is not a simple job for ordinary people. The goal of this thesis is to make people’s photos look more beautiful. Without any special skills, as long as you give our system your photos, we will help you to get your beautified photos. To reach this goal, our system was divided into two parts, one is the Rating System, and another is the Beautifying System. The rating system can evaluate a full-face photo, just like what we human usually do. We use the Support Vector Regression (SVR) to train an evaluation model, based on 213 full-face photos, and another 35 people to evaluate them. With this model our system can rate photos like a human, since our rating system achieves a correlation of 0.64 compare to human rating, which is comparable to the average human V.S. human rating of 0.68. With the previous scoring system, we further use a greedy algorithm to beautify photos. We slightly modify 36 feature points (grouped by heuristics) from the source to the target and try to get better rating by hill climbing. With the higher rating feature points, we can simply warp the source image to the resulting target image. After our beautifying procedure, we can in average increase 1.37 rating points in our rating system (score ranges from 1 to 7 with 7 the best score).