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基于局部形状约束网络的人脸对齐
引用本文:杜文超,邓宗平,赵启军,陈虎.基于局部形状约束网络的人脸对齐[J].四川大学学报(自然科学版),2017,54(5):953-958.
作者姓名:杜文超  邓宗平  赵启军  陈虎
作者单位:四川大学,四川省成都市四川大学计算机学院,四川省成都市四川大学计算机学院,四川省成都市四川大学计算机学院
基金项目:国家自然科学基金,其它
摘    要:基于级联回归的人脸对齐方法已经取得了很大的成就,但是由于复杂的级联回归器设计、人为设计特征等局限性的影响使得人脸对齐没有找到一个性能更好的解决方案,尤其对于大姿态、大表情等条件下的人脸对齐任务.因此,为解决该问题,提出了一种新颖的人脸对齐方法——基于人脸局部形状约束.首先利用卷积神经网络初始化人脸整体形状;然后利用人脸局部区域的同质性,将人脸区域进行划分,对每一个区域定义局部形状约束;最后再由整体形状估计做为全局约束,组合各个面部局部形状约束,对整体面部特征点进行回归.实验结果表明,该方法提高了人脸对齐的精确度且速度上达到了实时.

关 键 词:人脸对齐  级联回归  局部形状约束  卷积神经网络
收稿时间:2016/12/16 0:00:00
修稿时间:2017/2/11 0:00:00

Face Alignment Based on Local Shapes Constraint Network
DU Wen-Chao,DENG Zong-Ping,ZHAO Qi-Jun and CHEN Hu.Face Alignment Based on Local Shapes Constraint Network[J].Journal of Sichuan University (Natural Science Edition),2017,54(5):953-958.
Authors:DU Wen-Chao  DENG Zong-Ping  ZHAO Qi-Jun and CHEN Hu
Institution:School of Computer Science, Sichuan University, Chengdu,School of Computer Science, Sichuan University, Chengdu,School of Computer Science, Sichuan University, Chengdu
Abstract:Face alignment method has greatly improved with using cascade regression, however due to the complexity of designing cascade regressors, the limitation of hand-crafted features make it difficult to find a better solution for face alignment task, especially with the big gesture, exaggerated expressions. Therefore, this paper proposes a novel method based on local shape constraint to solve this problem. Firstly it initializes the whole face shape by using deep convolutional neural networks (DCNN), secondly divides face into different regions according to the local regional homogeneity, defining each region constraints on local shapes, Finally takes the whole shape estimation as global constraints, combines each local shape constraints for facial feature point regression. The experiments show that our method based on local shapes constraint results in a strong improvement over the current state-of-the-art.
Keywords:face alignment  cascade regression  local shape constraint  convolutional neural network
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