From one to many: Pose-Aware Metric Learning for single-sample face recognition
作者：
Highlights：
• Extended generic elastic model synthesizes facial images under varying 3D shape (depth) and illumination variations from a single gallery image.
• Pose-Aware Metrics are individually learnt by linear regression analysis at every quantized pose.
• PAML does not rely on any external multi-poses training set.
• Experiments on Multi-PIE database show 100% accuracy of PAML on the test setting across poses.
• PAML outperforms the deep learning approaches by over 10% accuracy for recognition across poses and illuminations.
摘要
•Extended generic elastic model synthesizes facial images under varying 3D shape (depth) and illumination variations from a single gallery image.•Pose-Aware Metrics are individually learnt by linear regression analysis at every quantized pose.•PAML does not rely on any external multi-poses training set.•Experiments on Multi-PIE database show 100% accuracy of PAML on the test setting across poses.•PAML outperforms the deep learning approaches by over 10% accuracy for recognition across poses and illuminations.
论文关键词：Face recognition,Single sample per person,Metric learning,3D generic elastic model,Face re-rendering,3D face construction
论文评审过程：Received 10 September 2016, Revised 26 September 2017, Accepted 16 October 2017, Available online 17 October 2017, Version of Record 6 February 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.10.020
