结合径向线的局部遮挡下的3维人脸识别
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摘要
目的 3维人脸点云的局部遮挡是影响3维人脸识别精度的一个重要因素。为克服局部遮挡对3维人脸识别的影响,提出一种基于径向线和局部特征的3维人脸识别方法。方法首先为了充分利用径向线的邻域信息,提出用一组局部特征来表示径向线;其次对于点云稀疏引起的采样点不均匀,提出将部分相邻局部区域合并以减小采样不均匀的影响;然后,利用径向线的邻域信息构造代价函数,进而构造相应径向线间的相似向量。最后,利用相似向量来进行径向线匹配,从而完成3维人脸识别。结果在FRGC v2.0数据库上进行不同局部特征识别率的测试实验,选取的局部特征Rank-1识别率达到了95.2%,高于其他局部特征的识别率;在Bosphorus数据库上进行不同算法局部遮挡下的人脸识别实验,Rank-1识别率达到了最高的92.0%;进一步在Bosphorus数据库上进行不同算法的时间复杂度对比实验,耗费时间最短,为8.17 s。该算法在准确率和耗时方面均取得了最好的效果。结论基于径向线和局部特征的3维人脸方法能有效提取径向线周围的局部信息;局部特征的代价函数生成的相似向量有效减小了局部遮挡带来的影响。实验结果表明本文算法具有较高的精度和较短的耗时,同时对人脸的局部遮挡具有一定的鲁棒性。该算法适用于局部遮挡下的3维人脸识别,但是对于鼻尖部分被遮挡的人脸,无法进行识别。
Objective Face recognition technology has extensive applications in many fields because of its user-friendly and intuitive nature. According to input data,face recognition can be divided into 2D and 3D. Traditional 2D face recognition technology is based on image or video information. Although 2D face recognition technology has achieved a great success,its limitations,which are mostly caused by illumination,posture,and makeup,remain difficult to address. Unlike traditional 2D face recognition,3D face recognition is based on the 3D data of human face,such as 3D point cloud and mesh.Although 3D face recognition technology is less affected by illumination,pose,and makeup,partial occlusion in 3D face is an important factor that affects its accuracy rate. The problem is that collected face data are always occluded by external objects,such as hands,hair,and glasses. Therefore,3D face recognition with partial occlusions becomes an important research subject. To reduce the influence of partial occlusions in 3D face recognition,a novel 3D face recognition algorithm based on radial strings and local feature is proposed. Method This 3D face recognition algorithm includes four main parts.First,the nasal tip on 3D face data using shape index is located,the radial strings are extracted,and then the uniform sampling on every radial strings is produced. To fully use the neighbor information of radial strings,a radial string representation that encoded radial strings into local feature is proposed. In this algorithm,we extract three local features,namely,the center of every two adjacent sample points,the area of local region,and the histogram of slant angle. Local feature descriptors with these local features to represent local region are then constructed. Second,sparse cloud points lead to nonuniform sample points and subsequently to large errors in the matching result. To address this problem,an operator that merges adjacent local regions is adopted. Third,cost function of the local feature on the corresponding local region and similarity vectors of the corresponding radial strings with this cost function are constructed. Finally,the corresponding radial strings according to these similarity vectors are matched,and the 3D face by the result of all radial strings are recognized. Result The experiments are conducted on the basis of the FRGC v2. 0 and Bosphorus databases. FRGC v2. 0 is a large-scale public 3D face database,which is composed of 466 subjects and 4007 3D point cloud. Bosphorus database is a new 3D face database,which is composed of 105 subjects and 4666 3D point cloud,and this database consists of partial occlusions at different levels. We select 300 subjects with neutral and not occluded 3D face point cloud to test the recognition rate of different local features. Consequently,the rank one recognition rate is 95. 2%,0. 9%,and 2. 4% higher than the other two local features,because FRGC v2. 0 database is standard and at a high level. Although one local feature is only 0. 9% higher than the second local feature,those local features promote the convenience of merging adjacent local regions. We then choose300 3D face point cloud from Bosphorus database to perform the experiments of recognition rate and time with partial occlusions 3D face. The rank one recognition rate is 92. 0%,2. 7%,3. 0%,and 0. 4% higher than the other three recognition methods. The experiment is performed within 8. 17 s,which is lower than that of the other recognition methods,and is2. 05 s,0. 18 s,and 34. 43 s less than the other three methods,respectively. In those experiments,our methods receive the best result based on its recognition rate and recognition time. Conclusion The proposed method of 3D face recognition based on radial strings and local feature extracts the adjacent information of radial strings effectively,thereby constructing cost function of corresponding local regions with the adjacent information to achieve region matching. The similarity vector that is constructed using the cost function of the local feature reduces the influence of partial occlusions effectively. This result demonstrates that the proposed algorithm achieves high recognition rates and is robust to partial occlusions. This 3D face recognition method is suitable in recognizing faces with partial occlusions. However,this method is inapplicable when the nasal tip is occluded because this method must locate the position of nasal tip.
Objective Face recognition technology has extensive applications in many fields because of its user-friendly and intuitive nature. According to input data,face recognition can be divided into 2D and 3D. Traditional 2D face recognition technology is based on image or video information. Although 2D face recognition technology has achieved a great success,its limitations,which are mostly caused by illumination,posture,and makeup,remain difficult to address. Unlike traditional 2D face recognition,3D face recognition is based on the 3D data of human face,such as 3D point cloud and mesh.Although 3D face recognition technology is less affected by illumination,pose,and makeup,partial occlusion in 3D face is an important factor that affects its accuracy rate. The problem is that collected face data are always occluded by external objects,such as hands,hair,and glasses. Therefore,3D face recognition with partial occlusions becomes an important research subject. To reduce the influence of partial occlusions in 3D face recognition,a novel 3D face recognition algorithm based on radial strings and local feature is proposed. Method This 3D face recognition algorithm includes four main parts.First,the nasal tip on 3D face data using shape index is located,the radial strings are extracted,and then the uniform sampling on every radial strings is produced. To fully use the neighbor information of radial strings,a radial string representation that encoded radial strings into local feature is proposed. In this algorithm,we extract three local features,namely,the center of every two adjacent sample points,the area of local region,and the histogram of slant angle. Local feature descriptors with these local features to represent local region are then constructed. Second,sparse cloud points lead to nonuniform sample points and subsequently to large errors in the matching result. To address this problem,an operator that merges adjacent local regions is adopted. Third,cost function of the local feature on the corresponding local region and similarity vectors of the corresponding radial strings with this cost function are constructed. Finally,the corresponding radial strings according to these similarity vectors are matched,and the 3D face by the result of all radial strings are recognized. Result The experiments are conducted on the basis of the FRGC v2. 0 and Bosphorus databases. FRGC v2. 0 is a large-scale public 3D face database,which is composed of 466 subjects and 4007 3D point cloud. Bosphorus database is a new 3D face database,which is composed of 105 subjects and 4666 3D point cloud,and this database consists of partial occlusions at different levels. We select 300 subjects with neutral and not occluded 3D face point cloud to test the recognition rate of different local features. Consequently,the rank one recognition rate is 95. 2%,0. 9%,and 2. 4% higher than the other two local features,because FRGC v2. 0 database is standard and at a high level. Although one local feature is only 0. 9% higher than the second local feature,those local features promote the convenience of merging adjacent local regions. We then choose300 3D face point cloud from Bosphorus database to perform the experiments of recognition rate and time with partial occlusions 3D face. The rank one recognition rate is 92. 0%,2. 7%,3. 0%,and 0. 4% higher than the other three recognition methods. The experiment is performed within 8. 17 s,which is lower than that of the other recognition methods,and is2. 05 s,0. 18 s,and 34. 43 s less than the other three methods,respectively. In those experiments,our methods receive the best result based on its recognition rate and recognition time. Conclusion The proposed method of 3D face recognition based on radial strings and local feature extracts the adjacent information of radial strings effectively,thereby constructing cost function of corresponding local regions with the adjacent information to achieve region matching. The similarity vector that is constructed using the cost function of the local feature reduces the influence of partial occlusions effectively. This result demonstrates that the proposed algorithm achieves high recognition rates and is robust to partial occlusions. This 3D face recognition method is suitable in recognizing faces with partial occlusions. However,this method is inapplicable when the nasal tip is occluded because this method must locate the position of nasal tip.
引文
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[18]Savran A,Alyüz N,Dibekliogˇlu K,et al.Bosphorus database for 3D face analysis[C]//Proceedings of the 1st European Workshop on Biometrics and Identity Management.Roskilde,Denmark:Springer,2008:47-56.
[19]Colombo A,Cusano C,Schettini R.Detection and restoration of occlusions for 3D face recognition[C]//Proceedings of 2006IEEE International Conference on Multimedia and Expo.Toronto,Ont.,Canada:IEEE,2006:1541-1544.[DOI:10.1109/ICME.2006.262837]
[2]Zhu L,Zhu S A.Face recognition based on two-dimensional image principal component analysis[J].Journal of Zhejiang University:Engineering Science,2007,41(2):264-267.[祝磊,朱善安.基于2维广义主成分分析的人脸识别[J].浙江大学学报:工学版,2007,41(2):264-267.][DOI:10.3785/j.issn.1008-973X.2007.02.016]
[3]Chang P P,Da F P,Mei J.Expression-robustness 3D face recognition based on facial profiles[J].Journal of Image and Graphics,2015,20(3):332-339.[常朋朋,达飞鹏,梅俊.对表情鲁棒的面部轮廓线3维人脸识别[J].中国图象图形学报,2015,20(3):332-339.][DOI:10.11834/jig.20150304]
[4]Wang Y M,Pan G,Wu Z H.A survey of 3D face recognition[J].Journal of Computer-Aided Design and Computer Graphics,2008,20(7):819-829.[王跃明,潘纲,吴朝晖.3维人脸识别研究综述[J].计算机辅助设计与图形学学报,2008,20(7):819-829.]
[5]Berretti S,Werghi N,Del Bimbo A,et al.Matching 3D face scans using interest points and local histogram descriptors[J].Computers&Graphics,2013,37(5):509-525.[DOI:10.1016/j.cag.2013.04.001]
[6]Alyuz N,Gokberk B,Akarun L.3-D face recognition under occlusion using masked projection[J].IEEE Transactions on Information Forensics and Security,2013,8(5):789-802.[DOI:10.1109/TIFS.2013.2256130]
[7]Colombo A,Cusano C,Schettini R.Gappy PCA classification for occlusion tolerant 3D face detection[J].Journal of Mathematical Imaging and Vision,2009,35(3):193-207.[DOI:10.1007/s10851-009-0165-y]
[8]Drira H,Ben Amor B,Srivastava A,et al.3D face recognition under expressions,occlusions,and pose variations[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(9):2270-2283.[DOI:10.1109/TPAMI.2013.48]
[9]Yu X,Gao Y S,Zhou J.3D face recognition under partial occlusions using radial strings[C]//Proceedings of 2016 IEEE International Conference on Image Processing.Phoenix,Arizona,USA:IEEE,2016:3016-3020.[DOI:10.1109/ICIP.2016.7532913]
[10]Visvalingam M,Whyatt J D.The Douglas-Peucker algorithm for line simplification:re-evaluation through visualization[C]//Computer Graphics Forum.Blackwell Publishing Ltd.1990,9(3):213-228.
[11]Tsai W H,Yu S S.Attributed string matching with merging for shape recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1985,PAMI-7(4):453-462.[DOI:10.1109/TPAMI.1985.4767684]
[12]Guo M L,Da F P,Deng X,et al.3D face recognition based on keypoints and local feature[J].Journal of Zhejiang University:Engineering Science,2017,51(3):584-589.[郭梦丽,达飞鹏,邓星,等.基于关键点和局部特征的3维人脸识别[J].浙江大学学报:工学版,2017,51(3):584-589.][DOI:10.3785/j.issn.1008-973X.2017.03.021]
[13]Tang L L,Gai S Y,Da F P,et al.3D Face recognition method based on the local binary pattern from vertical and horizontal on the mesh[J].Chinese Journal of Scientific Instrument,2016,37(6):1413-1420.[汤兰兰,盖绍彦,达飞鹏,等.基于网格纵横局部二值模式的3维人脸识别[J].仪器仪表学报,2016,37(6):1413-1420.][DOI:10.3969/j.issn.0254-3087.2016.06.027]
[14]Pan G,Wang Y M,Qi Y P,et al.Finding symmetry plane of3D face shape[C]//Proceedings of the 18th International Conference on Pattern Recognition.Hong Kong,China:IEEE,2006:1143-1146.[DOI:10.1109/ICPR.2006.565]
[15]Li X L,Da F P.Efficient 3D face recognition handling facial expression and hair occlusion[J].Image and Vision Computing,2012,30(9):668-679.[DOI:10.1016/j.imavis.2012.07.011]
[16]Faltemier T C,Bowyer K W,Flynn P J.A region ensemble for3-D face recognition[J].IEEE Transactions on Information Forensics and Security,2008,3(1):62-73.[DOI:10.1109/TIFS.2007.916287]
[17]Phillips P J,Flynn P J,Scruggs T,et al.Overview of the face recognition grand challenge[C]//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.San Diego,CA,USA:IEEE,2005:947-954.[DOI:10.1109/CVPR.2005.268]
[18]Savran A,Alyüz N,Dibekliogˇlu K,et al.Bosphorus database for 3D face analysis[C]//Proceedings of the 1st European Workshop on Biometrics and Identity Management.Roskilde,Denmark:Springer,2008:47-56.
[19]Colombo A,Cusano C,Schettini R.Detection and restoration of occlusions for 3D face recognition[C]//Proceedings of 2006IEEE International Conference on Multimedia and Expo.Toronto,Ont.,Canada:IEEE,2006:1541-1544.[DOI:10.1109/ICME.2006.262837]