学术报告第202136期

报告题目：Flexible and Consistent Face Alignment via Large Deformation Diffeomorphic Metric Mapping and Deep Learning

报 告 人：唐晓颖 副研究员（南方科技大学）

报告时间：4月6日9:30

报告地点：南一楼中311会议室

主持人：陶文兵 教授

Abstract：Face alignment is a prerequisite in many computer vision tasks, such as face recognition, facial expression recognition, face verification, face reconstruction and face reenactment. Most existing face alignment methods can only deal with the specific annotation scheme adopted by the training dataset of interest, but cannot flexibly accommodate multiple annotation schemes. To address this problem, we innovatively propose a flexible and consistent face alignment framework, LDDMM-Face, the key contribution of which is a deformation layer that naturally embeds facial geometry in a diffeomorphic way. Instead of predicting facial landmarks via heatmap or coordinate regression, we formulate this task in a diffeomorphic registration manner and predict momenta that uniquely parameterize the deformation between initial boundary and true boundary, and then perform large deformation diffeomorphic metric mapping (LDDMM) simultaneously for curve and landmark to localize the facial landmarks. Due to the embedding of LDDMM into a deep network, LDDMM-Face can consistently annotate facial landmarks without ambiguity and flexibly handle various annotation schemes, and can even predict dense annotations from sparse ones. Our method can be easily integrated into various face alignment networks. We extensively evaluate LDDMM-Face on four benchmark datasets: 300W, WFLW, HELEN and COFW-68. LDDMM-Face is comparable or superior to state-of-the-art methods for traditional within-dataset and same-annotation settings, but truly distinguishes itself with outstanding performance when dealing with weakly-supervised learning (partial-to-full), challenging cases (e.g., occluded faces), and different training and prediction datasets. In addition, LDDMM-Face shows promising results on the most challenging task of predicting across datasets with different annotation schemes.

个人简介：唐晓颖，博士，南方科技大学副研究员、博士生导师，美国约翰霍普金斯大学电气与计算机工程系客座教授，美国卡内基梅隆大学电气与计算机工程系客座教授。于2011年获美国约翰霍普金斯大学电气与计算机工程系及应用数学与统计系双硕士学位，2014年获美国约翰霍普金斯大学电气与计算机工程系博士学位。2014年至2015年在美国约翰霍普金斯大学图像科学中心从事博士后研究工作，2015年至2016年在美国卡内基梅隆大学进行访问教学。研究领域为医学图像处理，多模态磁共振图像分析，统计形态分析，数据分析，模式识别，机器学习等。国家自然科学基金青年项目及面上项目负责人，国家重点研发专项课题负责人，深圳市基础研究面上项目负责人。在包括 Medical Image Analysis, Pattern Recognition, NeuroImage, Human Brain Mapping, Neuroinformatics等知名期刊上发表50余篇国际期刊论文，50余篇学会Proceedings论文，申请发明专利11项（授权3项）、软件著作权5项。担任MICCAI2019本地主席、MICCAI2019/MICCAI2020/IEEE ISBI2020领域主席、IEEE ISBI2018/IEEE EMBC2018/IEEE ISBI2019/IEEE EMBC2019/MICCAI2019/MICCAI2020/IEEE ISBI2020分会场主席。