research-article

A review of recent advances in 3D ear- and expression-invariant face biometrics

Authors:

Syed M. S. Islam,

Mohammed Bennamoun,

Robyn A. Owens,

Rowan DaviesAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 44, Issue 3

Article No.: 14, Pages 1 - 34

https://doi.org/10.1145/2187671.2187676

Published: 14 June 2012 Publication History

Abstract

Biometric-based human recognition is rapidly gaining popularity due to breaches of traditional security systems and the lowering cost of sensors. The current research trend is to use 3D data and to combine multiple traits to improve accuracy and robustness. This article comprehensively reviews unimodal and multimodal recognition using 3D ear and face data. It covers associated data collection, detection, representation, and matching techniques and focuses on the challenging problem of expression variations. All the approaches are classified according to their methodologies. Through the analysis of the scope and limitations of these techniques, it is concluded that further research should investigate fast and fully automatic ear-face multimodal systems robust to occlusions and deformations.

References

[1]

3drma. 1998. 3D-RMA: 3D database. http://www.sic.rma.ac.be/beumier/DB/3d_rma.html.

[2]

Abate, A., Nappi, M., Riccio, D., and Sabatino, G. 2007. 2D and 3D face recognition: A survey. Pattern Recogn. Lett. 28, 14, 1885--1906.

Digital Library

[3]

Al-Osaimi, F., Bennamoun, M., and Mian, A. 2009. An expression deformation approach to non-rigid 3D face recognition. Int. J. Comput. Vis. 81, 3, 302--316.

Digital Library

[4]

Amor, B. B., Ardabilian, M., and Chen, L. 2008. Toward a region-based 3D face recognition approach. In Proceedings of the Multimedia Conference and Expo'08. 101--104.

[5]

Ansari, S. and Gupta, P. 2007. Localization of ear using outer helix curve of the ear. In Proceedings of the International Conference on Computing: Theory and Applications'07. 688--692.

Digital Library

[6]

Bhanu, B. and Chen, H. 2008. 3D Ear Detection from Side Face Range Images. Springer.

[7]

Binghamton University. 2006. Binghamton University 3D facial expression (BU-3DFE). http://www.cs. binghamton.edu/lijun/Research/3DFE/3DFE_Analysis.html.

[8]

BioID AG. 2001. BioID face Ddatabase. http://support.bioid.com/downloads/facedb/index.php.

[9]

Biometric Consortium. 2009. Introduction to Biometrics. http://www.biometrics.org/html/introduction.html.

[10]

Bosphorus. 2008. The Bosphorus database. http://bosphorus.ee.boun.edu.tr/.

[11]

Bowyer, K., Chang, K., and Flynn, P. 2006a. A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition. Comput. Vis. Image Understand. 101, 1, 1--15.

Digital Library

[12]

Bowyer, K. W., Chang, K. I., Yan, P., Flynn, P. J., Hansley, E., and Sarkar, S. 2006b. Multi-Modal biometrics: An overview. In Proceedings of the 2^ndWorkshop on Multimodal User Authentication.

[13]

Bronstein, A., Bronstein, M., and Kimmel, R. 2006. Robust expression-invariant face recognition from partially missing data. In Proceedings of the European Conference on Computer Vision (ECCV'06). Lecture Notes in Computer Science. Springer, 396--408.

Digital Library

[14]

Campbell, R. J. and Flynn, P. J. 2001. A survey of free-form object representation and recognition techniques. Comput. Vis. Image Understand. 81, 2, 166--210.

Digital Library

[15]

Canny, J. 1986. Towards fast 3D ear recognition for real-life biometric applications. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679--714.

Digital Library

[16]

CAS-PEAL. 2004. CAS-PEAL face database. http://www.jdl.ac.cn/peal/index.html.

[17]

Chang, K., Bowyer, K., and Flynn, P. 2005. Adaptive rigid multi-region selection for handling expression variation in 3D face recognition. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR'05). 157.

Digital Library

[18]

Chang, K., Bowyer, K., and Flynn, P. 2006. Multiple nose region matching for 3D face recognition under varying facial expression. IEEE Trans. Pattern Anal. Mach. Intell. 28, 10, 1695--1700.

Digital Library

[19]

Chang, K., Bowyer, K. W., Sarkar, S., and Victor, B. 2003. Comparison and combination of ear and face images for appearance-based biometrics. IEEE Trans. Pattern Anal. Mach. Intell. 25, 9, 1160--1165.

Digital Library

[20]

Chen, H. and Bhanu, B. 2004. Human ear detection from side face range images. In Proceedings of the International Conference on Pattern Recognition (ICPR'04). 574--577.

Digital Library

[21]

Chen, H. and Bhanu, B. 2005. Shape model-based 3D ear detection from side face range images. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). 122.

Digital Library

[22]

Chen, H. and Bhanu, B. 2007. Human ear recognition in 3D. IEEE Trans. Pattern Anal. Mach. Intell. 29, 4, 718--737.

Digital Library

[23]

Chen, H. and Bhanu, B. 2009. Efficient recognition of highly similar 3D objects in range images. IEEE Trans. Pattern Anal. Mach. Intell. 31, 1, 172--179.

Digital Library

[24]

Chen, H.-Y., Huang, C.-L., and Fu, C.-M. 2008. Hybrid-Boost learning for multi-pose face detection and facial expression recognition. Pattern Recogn. 41, 3, 1173--1185.

Digital Library

[25]

Choraś, M. 2005. Ear biometrics based on geometrical feature extraction. Electron. Lett. Comput. Vis. Image Anal. 5, 84--95.

[26]

Choraś, M. 2007a. Emerging methods of biometrics human identification. In Proceedings of the 2^nd International Conference on Innovative Computing, Information and Control (ICICIC'07). 365.

Digital Library

[27]

Choraś, M. 2007b. Image feature extraction methods for ear biometrics: A survey. In Proceedings of the 6^th International Conference on Computer Information Systems and Industrial Management Applications. 261--265.

Digital Library

[28]

Chua, C., Han, F., and Ho, Y. 2000. 3D human face recognition using point signatures. In Proceedings of the IEEE Conference on Analysis and Modeling of Faces and Gestures. 233--238.

Digital Library

[29]

Chua, C. S. and Jarvis, R. 1997. Point signatures: A new representation for 3D object recognition. Int. J. Comput. Vis. 25, 1, 63--85.

Digital Library

[30]

CIFAS. 2010. 2009 fraud trends. http://www.cifas.org.uk.

[31]

CMU. 2000. PIE database. http://www.ri.cmu.edu/research_project_detail.html&quest;project_id=418&menu_id= 261.

[32]

CNET. CNET tv news: ZCam. http://www.cnettv.cnet.com/3dv-systems-zcam/9742-153-31876.html.

[33]

Colombo, A., Cusano, C., and Schettini, R. 2007. Face3 a 2D+3D robust face recognition system. In Proceedings of the International Conference on Image Analysis and Processing (ICIAP'07). 393--398.

Digital Library

[34]

Delac, K., Grgic, M., and Bartlett, M. S. E. 2008. Recent Advances in Face Recognition. IN TECH, Vienna, Austria.

[35]

Doral, C. and Jain, A. 1997. COSMOS: A representation scheme for 3D free-form objects. IEEE Trans. Pattern Anal. Mach. Intell. 19, 10, 1115--1130.

Digital Library

[36]

Faltemier, T., Bowyer, K., and Flynn, P. 2007. Using a multi-instance enrollment representation to improve 3D face recognition. In Proceedings of the IEEE International Conference on Biometrics: Theory, Applications and Systems. 1--6.

[37]

FERET. 2003. The color FERET database. http://face.nist.gov/colorferet/.

[38]

Frischholz, R. 2008. Face detection homepage. http://www.facedetection.com/homepage.htm.

[39]

Frischholz, R. and Dieckmann, U. 2000. Bioid: A multimodal biometric identification system. IEEE Comput. 33, 2, 64--68.

Digital Library

[40]

Gao, Y. and Maggs, M. 2005. Feature-Level fusion in personal identification. In Proceedings of the Conference on Computer Vision and Pattern Recogniton (CVPR'05). 468--473.

Digital Library

[41]

Ghiass, R. and Sadati, N. 2008. Multi-View face detection and recognition under variable lighting using fuzzy logic. In Proceedings of the IEEE International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). Vol. 1, 74--79.

[42]

Goldmann, L, Monich, U. J., and Sikora, T. 2007. Components and their topology for robust face detection in the presence of partial occlusions. IEEE Trans. Inf. Forensics Secur. 2, 3, 559--569.

Digital Library

[43]

Grgic, M. and Delac, K. 2009. Databases, face recognition homepage. http://www.face-rec.org/algorithms/.

[44]

Han, C. and Sim, K.-B. 2008. Real-Time face detection using AdaBoost algorithm. In Proceedings of the International Conference on Control, Automation and Systems (ICCAS). 1892--1895.

[45]

He, N., Sato, K., and Takahashi, Y. 2000. Partial face extraction and recognition using radial basis function networks. In Proceedings of the IAPR Workshop on Machine Vision Applications. 144--147.

[46]

Hjelmas, E. and Low, B. 2001. Face detection: A survey. Comput. Vis. Image Understand. 83, 3, 236--274.

Digital Library

[47]

Hotta, K. 2009. View independent face detection based on horizontal rectangular features and accuracy improvement using combination kernel of various sizes. Pattern Recogn. 42, 3, 437--444.

Digital Library

[48]

Huang, C., Ai, H., Li, Y., and Lao, S. 2007. High-Performance rotation invariant multiview face detection. IEEE Trans. Pattern Anal. Mach. Intell. 29, 4, 671--686.

Digital Library

[49]

Hurley, D. J., Arbab-Zavar, B., and Nixon, M. S. 2007. The ear as a biometric. In Proceedings of the EUSIPCO'07 Conference. 25--29.

[50]

Husken, M., Brauckmann, M., Gehlen, S., and Malsburg, C. 2005. Strategies and benefits of fusion of 2D and 3D face recognition. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR'05). 174.

Digital Library

[51]

Iannarelli, A. 1989. Ear Identification, Forensic Identification Series. Paramount, Fremont, CA.

[52]

ISL. 2009. Image databases. http://www.ecse.rpi.edu/~cvrl/database/database.html.

[53]

Islam, S., Bennamoun, M., and Davies, R. 2008a. Fast and fully automatic ear detection using cascaded AdaBoost. In Proceedings of the IEEE Workshop on Application of Computer Vision. 1--6.

Digital Library

[54]

Islam, S., Bennamoun, M., Mian, A., and Davies, R. 2008b. A fully automatic approach for human recognition from profile images using 2D and 3D ear data. In Proceedings of the International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT). 131--141.

[55]

Islam, S., Bennamoun, M., Mian, A., and Davies, R. 2009. Score level fusion of ear and face local 3D features for fast and expression-invariant human recognition. In Proceedings of the International Conference on Image Analysis and Recognition (ICIAR'09). Lecture Notes in Computer Science, vol. 5627. Springer, 387--396.

Digital Library

[56]

Islam, S., Bennamoun, M., Owens, R., and Davies, R. 2007. Biometric approaches of 2D-3D ear and face: A survey. In Proceedings of the International Conference on Systems, Computing Sciences and Software Engineering.

[57]

Islam, S., Bennamoun, M., Owens, R., and Davies, R. 2008c. Biometric approaches of 2D-3D ear and face: A survey. In Advances in Computer and Information Sciences and Engineering, T. Sobh, Ed. Springer, 509--514.

[58]

Islam, S. and Davies, R. 2009. Refining local 3D feature matching through geometric consistency for robust biometric recognition. In Proceedings of the Conference on Digital Image Computing: Techniques and Applications (DICTA'09). 513--518.

Digital Library

[59]

Islam, S., Davies, R., Mian, A., and Bennamoun, M. 2008d. A fast and fully automatic ear recognition approach based on 3D local surface features. In Proceedings of the Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS'08), J. Blanc-Talon et al., Eds. Lecture Notes in Computer Science, vol. 5259. Springer, 1081--1092.

Digital Library

[60]

Jain, A. K., Nandakumar, K., and Ross, A. 2005. Score normalization in multimodal biometric systems. Pattern Recogn. 38, 12, 2270--2285.

Digital Library

[61]

Jain, A. K., Ross, A., and Pankanti, S. 2006. Biometrics: A tool for information security. IEEE Trans. Inf. Forensics Secur. 1, 2, 125--143.

Digital Library

[62]

Jain, A. K., Ross, A., and Prabhakar, S. 2004. An introduction to biometric recognition. IEEE Trans. Circ. Syst. Video Technol. 14, 1, 4--20.

Digital Library

[63]

Javelin. 2010. Javelin. 2010. The 2010 identity fraud survey report. http://www.idsafety.net/2010IDFraudReportRelease.pdf.

[64]

Johnson, A. E. and Herbert, M. 1999. Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans. Pattern Anal. Mach. Intell. 21, 5, 674--686.

Digital Library

[65]

Kakadiaris, I., Passalis, G., Toderici, G., Murtuza, M., Lu, Y., Karampatziakis, N., and Theoharis, T. 2007. Three-Dimensional face recognition in the presence of facial expressions: An automated deformable model approach. IEEE Trans. Pattern Anal. Mach. Intell. 29, 4, 640--649.

Digital Library

[66]

Koenderink, J. and Doorn, A. J. 1992. Surface shape and curvature scales. Image Vis. Comput. 10, 557--565.

Digital Library

[67]

Kong, S., Heo, J., an DAbidi, B., Paik, J., and Abidi, M. 2005. Recent advances in visual and infrared face recognition review. Comput. Vis. Image Understand. 97, 1, 103--135.

Digital Library

[68]

Kumar, A., Wong, D. C. M., Shen, H., and Jain, A. K. 2003. Personal verification using palmprint and hand geometry biometric. In Proceedings of the International Conference on Audio- and Video-Based Person Authentication. 668--675.

Digital Library

[69]

Li, C. and Barreto, A. 2006. An integrated 3D face-expression recognition approach. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'06). Vol. 3.

[70]

Li, S. Z. and Jain, A. K. 2005. Handbook of Face Recognition. Springer.

Digital Library

[71]

Li, X., Mori, G., and Zhang, H. 2006. Expression-Invariant face recognition with expression classification. In Proceedings of the Canadian Conference on Computer and Robot Vision. 77--83.

Digital Library

[72]

Li, Y., Gong, S., Sherrah, J., and Liddell, H. 2004. Support vector machine based multi-view face detection and recognition. Image Vis. Comput. 22, 5, 413--427.

[73]

Lienhart, R. and Maydt, J. 2002. An extended set of Haar-like features for rapid object detection. In Proceedings of the International Conference on Image Processing'02. Vol. 1, 900--903.

[74]

Lu, L., Zhang, X., Zhao, Y., and Jia, Y. 2007. Human identification based on 3D ear models. In Proceedings of the International Conference on Innovative Computing, Information and Control (ICICIC'060). 353--356.

[75]

Lu, X. and Jain, A. K. 2008. Deformation modeling for robust 3D face matching. IEEE Trans. Pattern Anal. Mach. Intell. 30, 8, 1346--1356.

Digital Library

[76]

Mamic and Bennamoun, M. 2002. Representation and recognition of 3D free-form objects. Digital Signal Process. 12, 1, 47--76.

[77]

Meynet, J., Popovici, V., and Thiran, J.-P. 2007. Face detection with boosted Guassian features. Pattern Recogn. 40, 8, 2283--2291.

Digital Library

[78]

Mian, A., Bennamoun, M., and Owens, R. 2006. A novel representation and feature matching algorithm for automatic pairwise registration of range images. Int. J. Comput. Vis. 66, 1, 19--40.

Digital Library

[79]

Mian, A., Bennamoun, M., and Owens, R. 2008. Keypoint detection and local feature matching for textured 3D face recognition. Int. J. Comput. Vis. 79, 1, 1--12.

Digital Library

[80]

Mian, A. S., Bennamoun, M., and Owens, R. 2007. An efficient multimodal 2D-3D hybrid approach to automatic face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29, 11, 1927--1943.

Digital Library

[81]

Middendorff, C., Bowyer, K., and Yan, P. 2007. Multi-Modal biometrics involving the human ear. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recogniton (CVPR'07). Vol. 3, 1--2.

[82]

Mikolajczyk, K. and Schmid, C. 2005. A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27, 10, 1615--1630.

Digital Library

[83]

Mita, T., Kaneko, T., and Hori, O. 2005. A detector tree of boosted classifiers for real-time object detection and tracking. In Proceedings of the IEEE International Conference on Computer Vision (ICCV'05). 1619--1626.

[84]

Moreno, A. and Sanchez, A. 2004. GavadDb: A 3D face database. In Proceedings of the Workshop on Biometrics and the Internet. 77--85.

[85]

Mpiperis, I., Malasloris, S., and Strintzis, M. 2007. 3D face recognition by point signatures and iso-contours. In Proceedings of the IASTED International Conference on Signal Processing, Pattern Recognition and Applications (SPPRA'07). 233--238.

Digital Library

[86]

Mpiperis, I., Malassiotis, S., and Strintzis, M. 2008. Bilinear models for 3D face and facial expression recognition. IEEE Trans. Inf. Forensics Secur. 3, 3, 498--511.

Digital Library

[87]

Nair, P. and Cavallaro, A. 2009. 3D face detection, landmark localization, and registration using a point distribution model. IEEE Trans. Multimedia 11, 4, 611--623.

Digital Library

[88]

Nilsson, M., Nordberg, J., and Claesson, I. 2007. Face detection using local SMQT features and split up snow classifier. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'07). Vol. 2, 589--592.

[89]

NIST-MID. 1994. NIST mugshot identification database. http://www.nist.gov/srd/nistsd18.htm.

[90]

OpenCV. OpenCV library. http://sourceforge.net/projects/opencvlibrary/.

[91]

Osuna, E., Freund, R., and Girosit, F. 1997. Training support vector machines: An application to face detection. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR'97). 130--136.

Digital Library

[92]

Otsu, N. 1979. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybernet. 9, 1, 62--66.

[93]

Pan, X., Cao, Y., Xu, X., Lu, Y., and Zhao, Y. 2008. The study of multimodal recognition based on ear and face. In Proceedings of the IEEE International Conference on Audio, Language and Image Processing (ICALP'08). 385--389.

[94]

Passalis, G., Kakadiaris, I., Theoharis, T., Toderici, G., and Murtuza, N. 2005. Evaluation of 3D face recognition in the presence of facial expressions: An annotated deformable model approach. In Proceedings of the IEEE Workshop on Face Recognition Grand Challenge Experiments. Vol. 3, 171--179.

Digital Library

[95]

Passalis, G., Kakadiaris, I., Theoharis, T., Toderici, G., and Papaioannou, T. 2007. Towards fast 3D ear recognition for real-life biometric applications. In Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS'07). Vol. 3, 39--44.

Digital Library

[96]

Pavani, S., Delgado, D., and Frangi, A. 2010. Haar-Like features with optimally weighted rectangles for rapid object detection. Pattern Recogn. 43, 1, 160--172.

Digital Library

[97]

Pears, N. 2008. RBF shape histograms and their application to 3D face processing. In Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition (FG'08). 1--8.

[98]

Pears, N. and Heseltine, T. 2006. Isoradius contours: New representations and techniques for 3D face matching and registration. In Proceedings of the International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT'06). 176--183.

Digital Library

[99]

Petrovska-Delacretaz, D., Lelandais, S., Colineau, J., Chen, L., Dorizzi, B., Ardabilian, M., Krichen, E., Mellakh, M.-A., Chaari, A., Guerfi, S., D'Hose, J., and Ben Amor, B. 2008. The IV2 multimodal biometric database (including iris, 2D, 3D, stereoscopic, and talking face data), and the IV2-2007 evaluation campaign. In Proceedings of the IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS'08). 1--7.

[100]

Phillips, P., Flynn, P., Scruggs, T., Bowyer, K., Chang, J., Hoffman, K., Marques, J., Min, J., and Worek, W. 2005. Overview of the face recognition grand challenge. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR'05). 947--954.

Digital Library

[101]

Pun, K. H. and Moon, Y. S. 2004. Recent advances in ear biometrics. In Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition. 164--169.

Digital Library

[102]

Ross, A. and Govindarajan, R. 2005. Feature level fusion using hand and face biometrics. In Proceedings of the SPIE Conference on Biometric Technology for Human Identification II. 196--204.

[103]

Ross, A. and Jain, A. K. 2003. Information fusion in biometrics. Pattern Recogn. Lett. 24, 13, 2115--2125.

Digital Library

[104]

Ross, A. and Jain, A. K. 2004. Multimodal biometrics: An overview. In Proceedings of the European Signal Processing Conference. 1221--1224.

[105]

Ross, A. A., Nandakumar, K., and Jain, A. K. 2006. Handbook of Multibiometrics. Springer.

Digital Library

[106]

Ruifrok, A., Scheenstra, A., and Veltkamp, R. C. 2005. A survey of 3D face recognition methods. In Proceedings of the Conference on Audio- and Video-Based Biometric Person Authentication (AVBPA'05). Lecture Notes in Computer Science, vol. 3546. Springer, 891--899.

Digital Library

[107]

Schapire, R. and Singer, Y. 1999. Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37, 3, 297--336.

Digital Library

[108]

Sochman, J. and Madas, J. 2004. AdaBoost with totally corrective updates for fast face detection. In Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition. 445--450.

Digital Library

[109]

Theoharis, T., Passalis, G., Toderici, G., and Kakadiaris, I. 2008. Unified 3D face and ear recognition using wavelets on geometry images. Pattern Recogn. 41, 3, 796--804.

Digital Library

[110]

Tsalakanido, F., Malassiotis, S., and Strintzis, M. 2007. A 3D face and hand biometric system for robust user-friendly authentication. Pattern Recogn. Lett. 28, 16, 2238--2249.

Digital Library

[111]

UND. 2004. University of Notre Dame biometrics database. http://www.nd.edu/cvrl/CVRL/Data_Sets.html.

[112]

UND. 2005. University of Notre Dame biometrics database. http://www.nd.edu/cvrl/CVRL/Data_Sets.html.

[113]

Ushmaev, O. and Novikov, S. 2006. Biometric fusion: Robust approach. In Proceedings of the International Workshop on Multimodal User Authentication (MMUA'06).

[114]

USTB. 2004. The USTB database III. http://www.ustb.edu.cn/resb/en/doc/Imagedb_123_intro_en.pdf.

[115]

Viola, P. and Jones, M. 2004. Robust real-time face detection. Int. J. Comput. Vis. 57, 2, 137--154.

Digital Library

[116]

Wang, Y., Pan, G., and Wu, Z. 2004. Sphere-Spin-Image: A viewpoint invariant surface representation for 3D face recognition. In Proceedings of the International Conference on Computational Science (ICCS'04). Lecture Notes in Computer Science, vol. 3037. Springer, 427--434.

[117]

Wang, Y., Pan, G., and Wu, Z. 2007. 3D face recognition in the presence of expression: A guidance-based constraint deformation approach. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR'07). 1--7.

[118]

Wang, Y., Tan, T., and Jain, A. K. 2003. Combining face and iris biometrics for identity verification. In Proceedings of the International Conference on Audio- and Video-Based Person Authentication. 805--813.

Digital Library

[119]

Woodard, D., Faltemier, T., Yan, P., Flynn, P., and Bowyer, K. 2006. A comparison of 3D biometric modalities. In Proceedings of the CVPR Workshop. 57--61.

Digital Library

[120]

Wu, H., Chen, Q., and Yachida, M. 1999. Face detection from color images using a fuzzy pattern matching method. IEEE Trans. Pattern Anal. Mach. Intell. 21, 6.

Digital Library

[121]

Wu, J., Brubaker, S., Mullin, M., and Rehg, J. 2008. Fast asymmetric learning for cascade face detection. IEEE Trans. Pattern Anal. Mach. Intell. 30, 3, 369--382.

Digital Library

[122]

Xiaohua, L., Lam, K.-M., Lansun, S., and Jiliu, Z. 2009. Face detection using simplified Gabor features and hierarchical regions in a cascade of classifiers. Pattern Recogn. Lett. 30, 8, 717--728.

Digital Library

[123]

Xiaoxun, Z. and Yunde, J. 2007. Symmetrical null space LDA for face and ear recognition. Neurocomput. 70, 4-6, 842--848.

Digital Library

[124]

Xu, X. and Mu, Z. 2007. Feature fusion method based on KCCA for ear and profile face based multimodal recognition. In Proceedings of the IEEE International Conference on Automation and Logistics. 620--623.

[125]

Xu, X.-N., Mu, Z.-C., and Yuan, L. 2007. Feature-Level fusion method based on KFDA for multimodal recognition fusing ear and profile face. In Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR'07). 1306--1310.

[126]

Yale. 1997. The Yale face database. http://cvc.yale.edu/projects/yalefaces/yalefaces.html.

[127]

Yan, J. 2007. Ensemble SVM regression based multi-view face detection system. In Proceedings of the IEEE Workshop on Machine Learning for Signal Processing. 163--169.

[128]

Yan, P. 2006. Ear biometrics in human identification. Ph.D. thesis, University of Notre Dame.

Digital Library

[129]

Yan, P. and Bowyer, K. W. 2005a. Ear biometrics using 2D and 3D images. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR'05). 121.

Digital Library

[130]

Yan, P. and Bowyer, K. W. 2005b. Empirical evaluation of advanced ear biometrics. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR'05). 41.

Digital Library

[131]

Yan, P. and Bowyer, K. W. 2005c. Multi-Biometric 2D and 3D ear detection. In Proceedings of the International Conference on Audio- and Video-Based Biometric Person Authentication (AVBPA'05), T. Kanade, A. Jain, and N. K. Ratha, Eds. Lecture Notes in Computer Science, vol. 3546. Springer, 503--512.

Digital Library

[132]

Yan, P. and Bowyer, K. W. 2007. Biometric recognition using 3D ear shape. IEEE Trans. Pattern Anal. Mach. Intell. 29, 8, 1297--1308.

Digital Library

[133]

Yang, M.-H., Kriegman, D., and Ahuja, N. 2002. Detecting faces in images: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 24, 1, 34--58.

Digital Library

[134]

Yap, M., Ugail, H., Zwiggelaar, R., Rajoub, B., Doherty, V., Appleyard, S., and Hurdy, G. 2009. A short review of methods for face detection and multifractal analysis. In Proceedings of the International Conference on CyberWorlds. 231--236.

Digital Library

[135]

Yuan, L., Mu, Z., and Liu, Y. 2006. Multimodal recognition using face profile and ear. In Proceedings of the 1^st International Symposium on SCAA. 887--891.

[136]

Yuan, L. and Zhang, F. 2009. Ear detection based on improved AdaBoost algorithm. In Proceedings of the International Conference on Machine Learning and Cybernetics. Vol. 4, 2414--2417.

[137]

Zhang, X. and Gao, Y. 2009. Face recognition across pose: A review. Pattern Recogn. 42, 11, 2876--2896.

Digital Library

[138]

Zhao, W., Chellappa, R., Rosenfeld, A., and Phillips, P. 2003. Face recognition: A literature survey. ACM Comput. Surv. 35, 4, 399--458.

Digital Library

[139]

Zhou, S. K., Chellappa, R., and Zhao, W. 2006. Unconstrained Face Recognition (International Series on Biometrics). Springer.

Digital Library

[140]

Zhou, X. and Bhanu, B. 2006. Integrating face and gait for human recognition. In Proceedings of the CVPR Workshop'06. 55.

Digital Library

Cited By

Mehta RUjjwal GKumar A(2024)An Efficient 3D Ear Recognition Method Using Point Set Registration ApproachSN Computer Science10.1007/s42979-024-03078-85:6Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1007/s42979-024-03078-8
Wersényi GScheper VSpagnol SEixelberger TWittenberg T(2023)Cost-effective 3D scanning and printing technologies for outer ear reconstruction: current statusHead & Face Medicine10.1186/s13005-023-00394-x19:1Online publication date: 27-Oct-2023
https://doi.org/10.1186/s13005-023-00394-x
Guo YWang HWang LLei YLiu LBennamoun M(2023)3D Face Recognition: Two Decades of Progress and ProspectsACM Computing Surveys10.1145/361586356:3(1-39)Online publication date: 5-Oct-2023
https://dl.acm.org/doi/10.1145/3615863
Show More Cited By

Index Terms

A review of recent advances in 3D ear- and expression-invariant face biometrics
1. Computing methodologies
  1. Machine learning

Recommendations

Expression-Invariant 3D Face Recognition Using Patched Geodesic Texture Transform
DICTA '10: Proceedings of the 2010 International Conference on Digital Image Computing: Techniques and Applications

Numerous methods have been proposed for the expression-invariant 3D face recognition, but a little attention is given to the local-based representation for the texture of the 3D images. In this paper, we propose an expression-invariant 3D face ...
Expression-invariant face recognition by facial expression transformations

In this paper, we present a method of expression-invariant face recognition that transforms input face image with an arbitrary expression into its corresponding neutral facial expression image. When a new face image with an arbitrary expression is ...
A bi-modal face recognition framework integrating facial expression with facial appearance

Among many biometric characteristics, the facial biometric is considered to be the least intrusive technology that can be deployed in the real-world visual surveillance environment. However, in facial biometric, little research attention has been paid ...

Comments

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 44, Issue 3

June 2012

344 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/2187671

Issue’s Table of Contents

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 June 2012

Accepted: 01 October 2010

Revised: 01 September 2010

Received: 01 November 2009

Published in CSUR Volume 44, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Australian Research Council

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

27
Total Citations
View Citations
1,280
Total Downloads

Downloads (Last 12 months)15
Downloads (Last 6 weeks)1

Reflects downloads up to 25 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Mehta RUjjwal GKumar A(2024)An Efficient 3D Ear Recognition Method Using Point Set Registration ApproachSN Computer Science10.1007/s42979-024-03078-85:6Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1007/s42979-024-03078-8
Wersényi GScheper VSpagnol SEixelberger TWittenberg T(2023)Cost-effective 3D scanning and printing technologies for outer ear reconstruction: current statusHead & Face Medicine10.1186/s13005-023-00394-x19:1Online publication date: 27-Oct-2023
https://doi.org/10.1186/s13005-023-00394-x
Guo YWang HWang LLei YLiu LBennamoun M(2023)3D Face Recognition: Two Decades of Progress and ProspectsACM Computing Surveys10.1145/361586356:3(1-39)Online publication date: 5-Oct-2023
https://dl.acm.org/doi/10.1145/3615863
Ganapathi IAli SPrakash SVu NWerghi N(2023)A Survey of 3D Ear Recognition TechniquesACM Computing Surveys10.1145/356088455:10(1-36)Online publication date: 2-Feb-2023
https://dl.acm.org/doi/10.1145/3560884
Samatas GPapakostas G(2022)Biometrics: Going 3DSensors10.3390/s2217636422:17(6364)Online publication date: 24-Aug-2022
https://doi.org/10.3390/s22176364
Wersenyi GWittenberg TSudar A(2022)Handheld 3D Scanning and Image Processing for Printing Body Parts - A Workflow Concept and Current Results2022 IEEE 1st International Conference on Internet of Digital Reality (IoD)10.1109/IoD55468.2022.9987113(000061-000068)Online publication date: 23-Jun-2022
https://doi.org/10.1109/IoD55468.2022.9987113
Attallah BBrik YNadir C(2022)Ear Recognition Using Ensemble of Deep Features and Machine Learning Classifiers2022 32nd International Conference on Computer Theory and Applications (ICCTA)10.1109/ICCTA58027.2022.10206252(68-73)Online publication date: 17-Dec-2022
https://doi.org/10.1109/ICCTA58027.2022.10206252
Mursalin MShamsul Islam SGilani S(2021)3D Morphable Ear Model: A Complete Pipeline from Ear Segmentation to Statistical Modeling2021 Digital Image Computing: Techniques and Applications (DICTA)10.1109/DICTA52665.2021.9647339(1-6)Online publication date: Nov-2021
https://doi.org/10.1109/DICTA52665.2021.9647339
Mursalin MIslam S(2021)Deep Learning for 3D Ear Detection: A Complete Pipeline From Data Generation to SegmentationIEEE Access10.1109/ACCESS.2021.31295079(164976-164985)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3129507
Mursalin MIslam S(2020)EpNet: A Deep Neural Network for Ear Detection in 3D Point CloudsAdvanced Concepts for Intelligent Vision Systems10.1007/978-3-030-40605-9_2(15-26)Online publication date: 10-Feb-2020
https://dl.acm.org/doi/10.1007/978-3-030-40605-9_2
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents