Data Augmentation for 3D Face Recognition

• Main Authors: Chien-Wei Yeh, 葉千瑋
• Other Authors: Jia-Ching Wang
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/9t64h5

碩士 === 國立中央大學 === 資訊工程學系 === 107 === In recent years, deep learning has important increased the performance of 2D face recognition systems with the use of large-scale labeled image data. Deep neural networks can be closely approaching human-level depend heavily on the amount and quality of facial training data. However, contrast with 2D face recognition, training discriminative deep features for 3D face recognition is very difficult. Because of the unavailability of large training datasets, recognition accuracies have already saturated on existing 3D face datasets due to their small gallery sizes. Unlike 2D photograph, the collection of annotated high-quality large 3D facial scan datasets cannot be sourced from the web. In this paper, we show that using synthetically generated data as CNN training dataset can effectively work for 3D face recognition by fine-tuning the CNN with real-world data. We propose a 3D augmentation method for enlarging 3D facial data, we can generate 3D facial data with arbitrary amounts of facial identities, facial expression and pose variations by using 3D morphable face model. Finally, in our experiment we use two real-world 3D facial datasets to be compared. Our method outperforms the 3D face recognition system training only with real-world dataset. As well as, we find the significant accuracy improvement with the help from synthetic 3D facial data.