| Summary: | 碩士 === 國立交通大學 === 多媒體工程研究所 === 97 === Human face prediction is an interesting task in many applications, such as medical science, forensic science, face synthesis, and identification. This thesis proposes a statistic method based on human face features, which is used for face aging simulation.
The existing age estimation methods are WAS (Weighted Appearance Specific), AAS (the Appearance and Age Specific Classifiers), and AGES (AGing pattErn Subspace) etc presently.
We adopt a method: parent-enhanced aging prediction for repairing the aging prediction result from AGES method. Since we use the FG-NET face image database and train them by PCA with missing data to predict aging human face, the results are not appropriate for those images which are not from the training samples. In addition, several face images of FG-NET database are blurred and lack details for aging texture. So we consider annexing several images from one’s parents to enhance his/her image detail display in our experiment.
Our experiment shows that the proposed method achieves more faithful and detailed aging simulation.
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