摘要：【中文】一种用于化妆人脸验证的多任务深度判别度量学习模型构建方法，针对人脸化妆会导致人脸验证方法性能的降低的问题，提出了融合Fisher判别分析的多任务深度判别度量学习模型MT‑DDML‑FDA，使用深度度量学习结构，通过共享一个网络层在多个任务之间学习共享的转换知识，来捕获不同任务的人脸图像之间的潜在识别信息。同时，MT‑DDML‑FDA使用Fisher判别分析将类内相关矩阵和类间相关矩阵引入该模型，使每一个任务具有良好的距离度量。实验证明，MT‑DDML‑FDA在真实化妆人脸数据集上能够有效提高人脸验证的性能。 【EN】A multitask depth discrimination metric learning model construction method for face makeup verification provides a multitask depth discrimination metric learning model MT-DDML-FDA fused with Fisher discrimination analysis aiming at the problem that face makeup can cause the performance reduction of a face verification method, and potential identification information between face images of different tasks is captured by sharing a network layer to learn shared conversion knowledge among multiple tasks by using a depth metric learning structure. Meanwhile, the MT-DDML-FDA introduces an intra-class correlation matrix and an inter-class correlation matrix into the model by using Fisher discriminant analysis, so that each task has a good distance measurement. Experiments prove that the MT-DDML-FDA can effectively improve the performance of face verification on a real makeup face data set.