| Summary: | 碩士 === 國立臺灣科技大學 === 電子工程系 === 105 === Vision based applications have become a trend in modern world, and among them, age estimation is a useful tool in applications, such as marketing, security, and entertainment. However, age estimation is still quite challenging, because there are many factors, such as environment, mental or physical conditions, would affect the human aging process. Moreover, common make-ups or accessories would occlude important features, such as wrinkles, in pure vision based age estimation systems. There have been many researchers on age estimation, and the proposed system falls in the category of feature-based methods. This thesis proposed a novel method to improve automatic age estimation from human faces. Three types of features extraction algorithms are used, such as Extended Curvature Gabor Filter (ECG), Completed Local Binary Pattern (CLBP), and Local Directional Pattern (LDP). While the ECG is applied to the entire human face, CLBP and LDP are only applied to blocks with randomized scales, positions and orientations. Then, Support Vector Machine (SVM) is used to estimate the age from combined feature vectors. The Mean Absolute Error of the proposed method is 4.49 years old, which is better than existing methods.
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