Bilayer Part-based Model for Facial Landmark Detection and Pose Estimation

• Main Authors: Kai-Hsiang Chang, 張凱翔
• Other Authors: Gee-Sern Hsu
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/14157924061690028082

碩士 === 國立臺灣科技大學 === 機械工程系 === 103 === Tree Structured model (TSM) is proven effective for face detection, landmark localization and pose estimation. It is a rare approach that can solve all three issues using one single unified model. However, it can be too slow to handle real-time applications because of the heavy computation involved. Besides, it cannot detect faces less than 80x80 in size. A bilayer structure, coined Bilayer Tree Structure Model(BTSM), is proposed in this study to solve these two issues. The BTSM has a downscaled model with fewer parts and trained on down-scaled samples, and therefore, can detect faces as small as 50x50. When the down-scaled model finds faces of sufficient sizes, it would activate a full-scaled model to locate more landmarks without performing convolution through the image pyramid. Compared on various databases, the BTSM can be 30x faster than the original TSM, while keeping almost all advantages of TSM the same.