基于三维形变模型的人脸姿势表情校正

doi:10.11896/j.issn.1002-137X.2019.06.039

Abstract

Abstract: Aiming at the problems such as poor robustness and computational complexity in face pose correction,a new facial pose and expression correction algorithmis was proposed.First,the Fast-SIC algorithm is adopted to improve the AAM model and to enhence the fitting efficiency.Then,based on the face alignment results,3D face reconstruction is performed.A BFM-3DMM model combining expression parameters into classical 3DMM model was proposed.However,the face corrected by the BFM-3DMM model is not smooth enough.Due to the fact that the SFS algorithm is not constrained by the original statistical model,this algorithm is applied to re-correct 2D face from BFM-3DMM model.The algorithm achieves good alignment and correction effects both on AFLW and LFPW,which are the two famous large face databases,as well as self-build face database.The experimental evaluation results show that the corrected 2D faces have smoother apperance and higher fidelity compared with classical 3DMM model,and can also retain image background information.

Key words: 3D Face reconstruction, 3D morphable model (3DMM), Active appearance model (AAM), Shape from shading algorithm (SFS)

CLC Number:

TP391

WANG Qian-qing, ZHANG Jing-lei. Face Pose and Expression Correction Based on 3D Morphable Model[J].Computer Science, 2019, 46(6): 263-269.

References

[1]WANG J T,ZHAO L,QI X B.Face recognition method based on adaptive 3D Morphable Model and Multiple Manifold Discriminant Analysis[J].Computer Science,2017,44(S1):232-235.(in Chinese)
王渐韬,赵丽,齐兴斌.自适应三维形变模型结合流形分析的人脸识别方法[J].计算机科学,2017,44(S1):232-235.
[2]HUANG F,TAN S B.Face feature location based on improved active appearance model algorithm[J].Computer Engineering and Applications,2015,51(16):204-209.(in Chinese)
黄飞,谭守标.基于改进主动表观模型算法的人脸特征定位[J].计算机工程与应用,2015,51(16):204-209.
[3]TZIMIROPOULOS G,PANTIC M.Fast Algorithms for Fitting Active Appeaance Models to Unconstrained Images[J].International Journal of Computer Vision,2017,122(1):17-33.
[4]TRAN A T,HASSNER T,MASI I,et al.Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2017:1493-1502.
[5]HU G,YAN F,KITTLER J,et al.Efficient 3D Morphable Face Model Fitting[J].Pattern Recognition,2017,67(C):366-379.
[6]GU Y R,QI R,WANG B Y.Research on 3D Face Attitude Correction Algorithm[J].Journal of Instrumentation and Instrument,2010,31(10):2291-2295.(in Chinese)
顾亦然,闵瑞,王保云.三维人脸姿态校正算法研究[J].仪器仪表学报,2010,31(10):2291-2295.
[7]FANG S Y,ZHOU D K,CAO Y P,et al.Positive Face Image Synthesis Based on Pose Estimation[J].Computer Engineering,2015,41(10):240-244.(in Chinese)
方三勇,周大可,曹元鹏,等.基于姿态估计的正面人脸图像合成[J].计算机工程,2015,41(10):240-244.
[8]DING L J,FENG H,HUANG Y.Geometric Algebra Invariant Method for 3D Face Attitude Correction[J].Microcomputer Systems,2015,36(1):177-181.(in Chinese)
丁立军,冯浩,黄宇.3D人脸姿态校正的几何代数不变量方法[J].小型微型计算机系统,2015,36(1):177-181.
[9]YANG B,MA L.Improvement of weak texture face image synthesis method based on deformation model[J].Computer Simulation,2016,33(9):248-251.(in Chinese)
杨勃,马禄.基于形变模型的弱纹理人脸图像合成方法改进[J].计算机仿真,2016,33(9):248-251.
[10]LUO Y,TAO Y,YANG G.Facial texture mapping and defor-mation based on facial feature constraints[J].Computer Engineering and Computer Engineering and Applications,2018,54(6):188-192,240.(in Chinese)
罗岱,陶洋,杨刚.基于面部特征约束的人脸纹理映射及变形[J].计算机工程与应用,2018,54(6):188-192,240.
[11]YIN X,XIANG Y,SOHN K,et al.Towards Large-Pose Face Frontalization in the Wild[C]∥International Conference on Computer Vision.2017.
[12]TZIMIROPOULOS G,PANTIC M.Optimization Problems for Fast AAM Fitting in-the-Wild[C]∥IEEE International Confe-rence on Computer Vision.IEEE Computer Society,2013:593-600.
[13]PAYSAN P,KNOTHE R,AMBERG B,et al.A 3d face model for pose and illumination invariant face recognition[C]∥Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance,2009(AVSS’09).IEEE,2009:296-301.
[14]ZHU X,LEI Z,YAN J,et al.High-fidelity Pose and Expression Normalization for face recognition in thewild[C]∥Computer Vision and Pattern Recognition.IEEE,2015:787-796.
[15]CAO C,WENG Y,ZHOU S,et al.Faceware house:a 3d facial expression database for visual computing[J].IEEE Transactions on Visualization and Computer Graphics,2014,20(3):413-425.
[16]CHU B,ROMDHANI S,CHEN L.3D-Aided Face Recognition Robust to Expression and Pose Variations[C]∥IEEE Confe-rence on Computer Vision and Pattern Recognition.IEEE Computer Society,2014:1907-1914.
[17]BELHUMEUR D,JACOBS D,KRIEGMAN,et al.Localizing parts of faces using a consensus of exemplars[C]∥IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2011.
[18]KÖSTINGER M,WOHLHART P,ROTH P M,et al.Annotated facial landmarks in the wild:A large-scale,real-world database for facial landmark localization[C]∥2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).IEEE,2011:2144-2151.
[19]ARASHLOO S R,KITTLER J.Efficient processing of MRFs for unconstrained-pose face recognition[C]∥IEEE Sixth International Conference on Biometrics:Theory,Applications and Systems.IEEE,2013:1-8.
[20]HASSNER T,HAREL S,PAZ E,et al.Effective face frontalization in unconstrained images[C]∥IEEE Conference on Compu-ter Vision and Pattern Recognition.IEEE Computer Society,2014:4295-4304.
[21]HUANG G B,MATTAR M A,LEE H,et al.Learning to align from scratch[C]∥International Conference on Neural Information Processing Systems.Curran Associates Inc.,2012:764-772.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

Face Pose and Expression Correction Based on 3D Morphable Model

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 1

Metrics

Comments

Recommended 0