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Unconstrained Ear Processing: What is Possible and What Must Be Done

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Signal and Image Processing for Biometrics

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 292))

Abstract

Ear biometrics, compared with other physical traits, presents both advantages and limits. First of all, the small surface and the quite simple structure play a controversial role. On the positive side, they allow faster processing than, say, face recognition, as well as less complex recognition strategies than, say, fingerprints. On the negative side, the small ear area itself makes recognition systems especially sensitive to occlusions. Moreover, the prominent 3D structure of distinctive elements like the pinna and the lobe makes the same systems sensible to changes in illumination and viewpoint. Overall, the best accuracy results are still achieved in conditions that are significantly more favorable than those found in typical (really) uncontrolled settings. This makes the use of this biometrics in real world applications still difficult to propose, since a commercial use requires a much higher robustness. Notwithstanding the mentioned limits, ear is still an attractive topic for biometrics research, due to other positive aspects. In particular, it is quite easy to acquire ear images remotely, and these anatomic features are also relatively stable in size and structure along time. Of course, as any other biometric trait, they also call for some template updating. This is mainly due to age, but not in the commonly assumed way. The apparent bigger size of elders’ ears with respect to those of younger subjects, is due to the fact that aging causes a relaxation of the skin and of some muscle-fibrous structures that hold the so called pinna, i.e. the most evident anatomical element of the ear. This creates the belief that ears continue growing all life long. On the other hand, a similar process holds for the nose, for which the relaxation of the cartilage tissue tends to cause a curvature downwards. In this chapter we will present a survey of present techniques for ear recognition, from geometrical to 2D-3D multimodal, and will attempt a reasonable hypothesis about the future ability of ear biometrics to fulfill the requirements of less controlled/covert data acquisition frameworks.

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References

  1. Abate AF, Nappi M, Riccio D, Ricciardi S (2006) Ear recognition by means of a rotation invariant descriptor. In: Proceedings of the 18th international conference on pattern recognition–ICPR 2006, vol 4, pp 437–440

    Google Scholar 

  2. Abate AF, Nappi M, Riccio D, De Marsico M (2007) Face, ear and fingerprint: designing multibiometric architectures. In: Proceedings of 14th international conference on image analysis and processing–ICIAP 2007, pp 437–442

    Google Scholar 

  3. Abaza A, Hebert C, Harrison MAF (2010) Fast learning ear detection for real-time surveillance. In: Proceedings of the 4th IEEE international conference on biometrics: theory applications and systems–BTAS 2010, pp 1–6

    Google Scholar 

  4. Abaza A, Ross A (2010) Towards understanding the symmetry of human ears: a biometric perspective. In: Proceedings of the 4th IEEE international conference on biometrics: theory applications and systems–BTAS 2010, pp 1–7

    Google Scholar 

  5. Abaza A, Ross A, Harrison MAF, Nixon MS (2013) A survey on ear biometrics. ACM Comput Surveys 45(2), Article 22

    Google Scholar 

  6. Abdel-Mottaleb M, Zhou J (2006) Human ear recognition from face profile images. In: Zhang D, Jain AK (eds) Proceedings of the international conference on biometrics–ICB 2006, LNCS 3832, pp 786 – 792

    Google Scholar 

  7. Akkermans AHM, Kevenaar TAM, Schobben DWE (2005) Acoustic ear recognition for person identification. In: Proceedings of the AutoID’05, pp 219–223

    Google Scholar 

  8. Alvarez L, Gonzalez E, Mazorra L (2005) Fitting ear contour using an ovoid model. In: Proceedings of the 39th annual international carnahan conference on security technology–CCST ’05, pp 145–148

    Google Scholar 

  9. Ansari S, Gupta P (2007) Localization of ear using outer helix curve of the ear. In: Proceedings of the international conference on computing: theory and applications–ICCTA 2007, pp 688–692

    Google Scholar 

  10. Arbab-Zavar B, Nixon MS, Hurley DJ (2007) On model-based analysis of ear biometrics. In: Proceedings of the 1st IEEE international conference on biometrics: theory, applications, and systems–BTAS 2007, pp 1–5

    Google Scholar 

  11. Arbab-Zavar B, Nixon MS (2008) Robust log-gabor filter for ear biometrics. In: Proceedings of the 19th international conference on pattern recognition–ICPR 2008, pp 1–4

    Google Scholar 

  12. Bach FR, Jordan MI (2003) Kernel independent component analysis. J Mach Learn Res 3:1–48

    MATH  MathSciNet  Google Scholar 

  13. Badrinath GS, Gupta P (2009) Feature level fused ear biometric system. In: Proceedings of the 7th international conference on advances in pattern recognition–ICAPR ’09, pp 197–200

    Google Scholar 

  14. Battisti F, Carli M, De Natale FGB, Neri AA (2012) Ear recognition based on edge potential function. In: Proceedings of the SPIE 8295, image processing: algorithms and systems X; and parallel processing for imaging applications II, Feb 9, p 829508. doi:10.1117/12.909082

  15. Bay H, Tuytelaars T, Van Gool L (2006) SURF: speeded up robust features. In: Proceedings of the 9th european conference on computer vision–ECCV 2006, pp 404–417

    Google Scholar 

  16. Bhanu B, Chen H (2003) Human ear recognition in 3D. In: Proceedings of multimodal user authentication workshop (MMUA), Santa Barbara, CA, pp 91–98

    Google Scholar 

  17. Brown M, Lowe DG (2002) Invariant features from interest point groups. In: Proceedings of the 13th British machine vision conference, pp 253–262

    Google Scholar 

  18. Burge M, Burger W (1997) Ear biometrics for machine vision. In: Proceedings of 21 workshop of the Austrian association for pattern recognition

    Google Scholar 

  19. Burge M, Burger W (1998) Ear biometrics. In: Jain AK, Bolle R, Pankanti S (eds) Biometrics: personal identification in networked society. Kluwer Academic Publishers, Boston, pp 273–286

    Google Scholar 

  20. Bustard JD, Nixon MS (2008) Robust 2D ear registration and recognition based on SIFT point matching. In: 2nd IEEE international conference on biometrics: theory, applications and systems–BTAS 2008, pp 1–6

    Google Scholar 

  21. Bustard JD, Nixon MS (2010) Toward unconstrained ear recognition from two-dimensional images. IEEE Trans Syst Man Cyber Part A Syst Human 40(3):486–494

    Article  Google Scholar 

  22. Cadavid S, Abdel-Mottaleb M (2007) Human identification based on 3D ear models. In: Proceedings of the 1st IEEE international conference on biometrics: theory, applications, and systems–BTAS 2007, pp 1–6

    Google Scholar 

  23. Cadavid S, Abdel-Mottaleb M (2008) 3-D ear modeling and recognition from video sequences using shape from shading. IEEE Trans Info Forensics Security 3(4):709–718

    Article  Google Scholar 

  24. Cadavid S, Mahoor MH, Abdel-Mottaleb M (2009) Multi-modal biometric modeling and recognition of the human face and ear. In: Proceedings of the IEEE international workshop on safety, security and rescue robotics–SSRR 2009, pp 1–6

    Google Scholar 

  25. Cai J, Goshtasby A (1999) Detecting human faces in color images. Image Vision Comput 18(1):63–75

    Article  Google Scholar 

  26. Chang K, Victor B, Bowyer KW, Sarkar S (2003) Comparison and combination of ear and face images in appearance-based biometrics. IEEE Trans Pattern Anal Mach Intell 25(8):1160–1165

    Article  Google Scholar 

  27. Chen H, Bhanu B (2004) Human ear detection from side face range images. In: Proceedings of the international conference on pattern recognition (ICPR 2004), vol 3, pp 574–577

    Google Scholar 

  28. Chen H, 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–workshops (CVPR 2005), p 122

    Google Scholar 

  29. Chen H, Bhanu B (2007) Human ear recognition in 3D. IEEE Trans Pattern Anal Mach Intell 29(4):718–737

    Article  Google Scholar 

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

    Article  Google Scholar 

  31. Choraś M, Choraś RS (2006) Geometrical algorithms of ear contour shape representation and feature extraction. In: Proceedings of the intelligent systems design and application–ISDA 2006, IEEE CS Press, vol II, pp 451–456, Jinan, China

    Google Scholar 

  32. Cummings AH, Nixon MS, Carter JN (2010) A novel ray analogy for enrollment of ear biometrics. In: Proceedings of the 4th IEEE international conference on biometrics: theory applications and systems–BTAS 2010, pp 1–6

    Google Scholar 

  33. Curless B (1999) From range scans to 3D models. ACM SIGGRAPH, Comput Graph 33(4): 38–41

    Google Scholar 

  34. Debevec P (1999) Image-based modeling, rendering and lighting. Comput Graph 33(4):46–50

    Article  Google Scholar 

  35. De Marsico M, Michele N, Riccio D (2010) HERO: human ear recognition against occlusions. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition workshops–CVPRW 2010, pp 178–183

    Google Scholar 

  36. Dong J, Mu Z (2008) Multi-pose ear recognition based on force field transformation. In: Proceedings of the 2nd international symposium on intelligent information technology application–IITA 2008, vol 3, pp 771–775

    Google Scholar 

  37. Faez K, Motamed S, Yaqubi M (2008) Personal verification using ear and palm-print biometrics. In: Proceedings IEEE international conference on systems, man and cybernetics–SMC 2008, pp 3727–3731

    Google Scholar 

  38. Fleck M, Forsyth D, Bregler C (1996) Finding naked people. In: Proceedings of the European conference on computer vision–ECCV 1996, vol 2, pp 592–602

    Google Scholar 

  39. Gnanasivam P, Muttan S (2011) Ear and fingerprint biometrics for personal identification. In: Proceedings of the international conference on signal processing, communication, computing and networking technologies–ICSCCN 2011, pp 347–352

    Google Scholar 

  40. Grabham NJ, Swabey MA, Chambers P, Lutman ME, White NM, Chad JE, Beeby SP (2013) An evaluation of otoacoustic emissions as a biometric. IEEE Trans Info Forensics Security 8(81):174–183

    Article  Google Scholar 

  41. Gutierrez L, Patricia M, Lopez M (2010) Modular neural network integrator for human recognition from ear images. In: Proceedings of the 2010 international joint conference on neural networks–IJCNN 2010, pp 1–5

    Google Scholar 

  42. Huang C, Lu G, Liu Y (2009) Coordinate direction normalization using point cloud projection density for 3D ear. In: Proceedings of the 4th international conference on computer sciences and convergence information technology–ICCIT 2009, pp 511–515

    Google Scholar 

  43. Huang Z, Liu Y, Li C, Yang M, Chen L (2013) A robust face and ear based multimodal biometric system using sparse representation. Pattern Recogn 46(8):2156–2168

    Article  Google Scholar 

  44. Hurley DJ, Nixon MS, Carter JN (1999) Force field energy functionals for image feature extraction. In: Proceedings of the British machine vision conference 1999–BMVC99 BMVA, pp 604–613

    Google Scholar 

  45. Hurley DJ, Nixon MS, Carter JN (2000) Automatic ear recognition by force field transformations. IEE colloquium on visual biometrics (Ref.No. 2000/018), pp 7/1–7/5

    Google Scholar 

  46. Hurley DJ, Nixon MS, Carter JN (2002) Force field energy functionals for image feature extraction. Image Vision Comput 20(5–6):311–317

    Article  Google Scholar 

  47. Hurley DJ, Nixon MS, Carter JN (2005) Force field feature extraction for ear biometrics. Comput Vision Image Underst 98:491–512

    Google Scholar 

  48. Iannarelli A (1989) Ear identification. In forensic identification series. Paramont Publishing Company, Fremont

    Google Scholar 

  49. Islam SMS, Bennamoun M, Davies R (2008) Fast and fully automatic ear detection using cascaded adaBoost. In: Proceedings of the IEEE workshop on applications of computer vision–WACV 2008, pp 1–6

    Google Scholar 

  50. Jain A, Bolle R, Pankanti S (eds) (1998) Biometrics: personal identification in networked society. Kluwer Academic Publishers, Boston

    Google Scholar 

  51. Javadtalab A, Abbadi L, Omidyeganeh M, Shirmohammadi S, Adams CM, El-Saddik A (2011) Transparent non-intrusive multimodal biometric system for video conference using the fusion of face and ear recognition. In: Proceedings of the 9th annual international conference on privacy security and trust–PST 2011, pp 87–92

    Google Scholar 

  52. Jeges E, Mate L (2006) Model-based human ear identification. In: Proceedings of the world automation congress–WAC, pp 1–6

    Google Scholar 

  53. Kisku DR, Mehrotra H, Gupta P, Sing JK (2009) SIFT-based ear recognition by fusion of detected keypoints from color similarity slice regions. In: Proceedings of the international conference on advances in computational tools for engineering applications–ACTEA 2009, pp 380–385

    Google Scholar 

  54. Kisku DR, Sing JK, Gupta P (2009) Multibiometrics belief fusion. In: Proceedings of the 2nd international conference on machine vision–ICMV 2009, pp 37–40

    Google Scholar 

  55. Kisku DR, Gupta S, Gupta P, Sing JK (2010) An efficient ear identification system. In: Proceedings of the 5th international conference on future information technology–futuretech 2010, pp 1–6

    Google Scholar 

  56. Klare BF, Burge MJ, Klontz JC, Vorder Bruegge RW, Jain AK (2012) Face recognition performance: role of demographic information. IEEE Trans Info Forensics Security 7(6):1789–1801

    Article  Google Scholar 

  57. Kumar A, Hanmandlu M, Kuldeep M, Gupta HM (2011) Automatic ear detection for online biometric applications. In: Proceedings of the 3rd national conference on computer vision, pattern recognition, image processing and graphics–NCVPRIPG 2011, pp 146–149

    Google Scholar 

  58. Kumar A, Wu C (2012) Automated human identification using ear imaging. Pattern Recogn 45:956–968

    Google Scholar 

  59. Lades M, Vorbruggen JC, Buhmann J, Lange J, Von Der Malsburg C, Wurtz RP, Konen W (1993) Distortion invariant object recognition in the dynamic link architecture. IEEE Trans Comput 42(3):300–311

    Article  Google Scholar 

  60. Liu W, Wang Y, Li SZ, Tan T (2004) Null space-based kernel fisher discriminate analysis for face recognition. In: Proceedings of the 6th IEEE international conference on automatic face and gesture recognition–FG 2004, pp 369–374

    Google Scholar 

  61. Liu H (2011) Multi-view ear recognition by patrial least square discrimination. In: Proceedings of the 3rd international conference on computer research and development–ICCRD 2011, vol 4, pp 200–204

    Google Scholar 

  62. Liu H, Zhang D (2011) Fast 3D point cloud ear identification by slice curve matching. In: Proceedings of the 3rd international conference on computer research and development–ICCRD 2011, vol 4, pp 224–228

    Google Scholar 

  63. Lu L, Zhang X, Zhao Y, Jia Y (2006) Ear recognition based on statistical shape model. In: Proceedings of the IEEE international conference on innovative computing, information and control, vol 3, pp 353–356

    Google Scholar 

  64. Messer K, Matas J, Kittler J, Luettin J, Maitre G (1999) XM2VTSDB: the extended M2VTS database. In: Proceedings of the international conference on audio- and video-based person authentication–AVBPA ’99, pp 72–77

    Google Scholar 

  65. Monwar MM, Gavrilova M, Wang Y (2011) A novel fuzzy multimodal information fusion technology for human biometric traits identification. In: Proceedings of the 10th IEEE international conference on cognitive informatics and cognitive computing–ICCI*CC 2011, pp 112–119

    Google Scholar 

  66. Morano RA, Ozturk C, Conn R, Dubin S, Zietz S, Nissanov J (1998) Structured light using pseudorandom codes. IEEE Trans Pattern Anal Mach Intell 20(3), pp 322–327

    Google Scholar 

  67. Monwar M, Gavrilova M (2008) FES: a system for combining face, ear and signature biometrics using rank level fusion. In: Proceedings of the 5th international conference on information technology: new generations–ITNG 2008, pp 922–927

    Google Scholar 

  68. Moreno B, Sanchez A, Velez JF (1999) On the use of outer ear images for personal identification in security applications. In: Proceedings of the IEEE 33rd annual international carnahan conference on security technology, pp 469–476

    Google Scholar 

  69. Nanni L, Lumini A (2007) A multi-matcher for ear authentication. Pattern Recogn Lett 28(16):2219–2226

    Google Scholar 

  70. Nanni L, Lumini A (2009) Fusion of color spaces for ear authentication. Pattern Recogn 42(9):1906–1913

    Google Scholar 

  71. Nanni L, Lumini A (2009) A supervised method to discriminate between impostors and genuine in biometry. Expert Syst Appl 36(7):10401–10407

    Google Scholar 

  72. Nosrati MS, Faez K, Faradji F (2007) Using 2D wavelet and principal component analysis for personal identification based On 2D ear structure. In: Proceedings of the international conference on intelligent and advanced systems–ICIAS 2007, pp 616–620

    Google Scholar 

  73. Passalis G, Kakadiaris IA, Theoharis T, Toderici G, 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 2007, pp 39–44

    Google Scholar 

  74. Pflug A, Busch C (2012) Ear biometrics: a survey of detection, feature extraction and recognition methods. IET Biometrics 1(2):114–129

    Article  Google Scholar 

  75. Prakash S, Jayaraman U, Gupta P (2009) A skin-color and template based technique for automatic ear detection. In: Proceedings of the 7th international conference on advances in pattern recognition–ICAPR 2009, pp 213–216

    Google Scholar 

  76. Prakash S, Jayaraman U, Gupta P (2009) Connected component based technique for automatic ear detection. In: Proceedings of the 16th IEEE international conference on image processing–ICIP 2009, pp 2741–2744

    Google Scholar 

  77. Prakash S, Gupta P (2011) An efficient ear recognition technique invariant to illumination and pose. Telecommun Syst 52(3):1–14 http://dx.doi.org/10.1007/s11235--011-9621-2

    Google Scholar 

  78. Prakash S, Gupta P (2012) A rotation and scale invariant technique for ear detection in 3D. Pattern Recogn Lett 33, pp 1924–1931

    Google Scholar 

  79. Raposo R, Hoyle E, Peixinho A, Proenca H (2011) UBEAR: A dataset of ear images captured on-the-move in uncontrolled conditions. In: Proceedings of the IEEE workshop on computational intelligence in biometrics and identity management–CIBIM 2011, pp 84–90

    Google Scholar 

  80. Reisfeld D, Wolfson H, Yeshurun Y (1995) Context-free attentional operators: the generalized symmetry transform. Int J Comput Vision 14(2):119–130

    Google Scholar 

  81. Riccio D, Tortora G, De Marsico M, Wechsler H (2012) EGA-ethnicity, gender and age, a pre-annotated face database. In: Proceedings of the 2012 IEEE workshop on biometric measurements and systems for security and medical applications–BioMS 2012, pp 38–45

    Google Scholar 

  82. Said EH, Abaza A, Ammar H (2008) Ear segmentation in color facial images using mathematical morphology. In: Proceedings of the biometrics symposium–BSYM 2008, pp 29–34

    Google Scholar 

  83. Samangooei S, Bustard JD, Seeley RD, Nixon MS, Carter JN (2011) Acquisition and analysis of a dataset comprising gait, ear and semantic data. In: Bhanu B, Govindaraju (eds) Multibiometrics for human identification, pp 277–301. Cambridge University Press, Cambridge

    Google Scholar 

  84. Sana A, Gupta P, Purkai R (2007) Ear biometrics: a new approach. In: P Pal (ed) Advances in pattern recognition. World Scientific Publishing, New York, pp 46–50

    Google Scholar 

  85. Seely RD, Samangooei S, Lee M, Carter JN, Nixon MS (2008) The University of Southampton multi-biometric tunnel and introducing a novel 3D gait dataset. In: Proceedings of the 2nd IEEE international conference on biometrics: theory, applications and systems–BTAS 2008, pp 1–6

    Google Scholar 

  86. Shailaja D, Gupta P (2006) A simple geometric approach for ear recognition. In: Proceedings of the 9th international conference on information technology–ICIT 2006, pp 164–167

    Google Scholar 

  87. Shih H-C, Ho CC, Chang H-T, Wu C-S (2009) Ear detection based on arc-masking extraction and AdaBoost polling verification. In: Proceedings of the 5th international conference on intelligent information hiding and multimedia signal processing–IIH-MSP 2009, pp 669–672

    Google Scholar 

  88. Sun C, Mu Z-C, Zeng H (2009) Automatic 3D ear reconstruction based on epipolar geometry. In: Proceedings of the 5th international conference on image and graphics–ICIG 2009, pp 496–500

    Google Scholar 

  89. Takala V, Ahonen T, Pietikäinen M (2005) Block-based methods for image retrieval using local binary patterns. In: Proceedings of the 14th scandinavian conference–SCIA 2005, LNCS 3540, pp 882–891

    Google Scholar 

  90. Victor B, Bowyer K, Sarkar S (2002) An evaluation of face and ear biometrics. In: Proceedings of 16th international conference on pattern recognition, vol 1, pp 429–432

    Google Scholar 

  91. Viola P, Jones MM (2004) Robust real-time face detection. Int J Comput Vision 57(2):137–154

    Google Scholar 

  92. Wang Y, Mu Z-C, Liu K, Feng J (2007) Multimodal recognition based on pose transformation of ear and face images. In: Proceedings of the international conference on wavelet analysis and pattern recognition–ICWAPR 2007, vol 3, pp 1350–1355

    Google Scholar 

  93. Wang Y, Mu Z-C, Zeng H (2008) Block-based and multi-resolution methods for ear recognition using wavelet transform and uniform local binary patterns. In: Proceedings of the 19th international conference on pattern recognition–ICPR 2008, pp 1–4

    Google Scholar 

  94. Wang X, Yuan W (2010) Gabor wavelets and general discriminant analysis for ear recognition. In: Proceedings of the 8th world congress on intelligent control and automation–WCICA 2010, pp 6305–6308

    Google Scholar 

  95. Wang Z-Q, Yan X-D (2011) Multi-scale feature extraction algorithm of ear image. In: Proceedings of the international conference on electric information and control engineering–ICEICE 2011, pp 528–531

    Google Scholar 

  96. Watabe D, Sai H, Sakai K, Nakamura O (2008) Ear biometrics using jet space similarity. In: Proceedings of the Canadian conference on electrical and computer engineering–CCECE 2008, pp 1259–1264

    Google Scholar 

  97. Watabe D, Sai H, Sakai K, Nakamura O (2011) Improving the robustness of single-view ear-based recognition under a rotated in depth perspective. In: Proceedings of the 2011 international conference on biometrics and kansei engineering–ICBAKE, pp 179–184

    Google Scholar 

  98. Woodard DL, Faltemier TC, Ping Y, Flynn PJ, Bowyer KW (2006) A comparison of 3D biometric modalities. In: Proceedings of the conference on computer vision and pattern recognition workshop–CVPRW 2006, p 57

    Google Scholar 

  99. Wu J, Brubaker SC, Mullin MD, Rehg JM (2008) Fast asymmetric learning for cascade face detection. IEEE Trans Pattern Anal Mach Intell 30(3):369–382

    Article  Google Scholar 

  100. Wu H-L, Wang Q, Shen H-J, Hu L-Y (2009) Ear identification based on KICA and SVM. In: Proceedings of the WRI global congress on intelligent systems–GCIS 2009, vol 4, pp 414–417

    Google Scholar 

  101. Xie Z-X, Mu Z-C (2007) Improved locally linear embedding and its application on multi-pose ear recognition. In: Proceedings of the international conference on wavelet analysis and pattern recognition–ICWAPR 2007, vol 3, pp 1367–1371

    Google Scholar 

  102. Xie Z-X, Mu Z-C (2008) Ear recognition using LLE and IDLLE algorithm. In: Proceedings of the 19th international conference on patten recognition–ICPR 2008, pp 1–4

    Google Scholar 

  103. Xu X-N, Zhichun Mu Z-C (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, pp 620–623

    Google Scholar 

  104. Xu X-N, Mu Z-C, 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 2007, vol 3, pp 1306–1310

    Google Scholar 

  105. Xu X-N, Mu Z-C (2007) Multimodal recognition based on fusion of ear and profile face. In: Proceedings of the 4th international conference on image and graphics–ICIG 2007, pp 598–603

    Google Scholar 

  106. Xu H, Mu Z-C (2008) Multi-pose ear recognition based on improved locally linear embedding. In: Proceedings of the congress on image and signal processing–CISP 2008, vol 2, pp 39–43

    Google Scholar 

  107. Yaghoubi Z, Faez K, Eliasi M, Eliasi A (2010) Multimodal biometric recognition inspired by visual cortex and support vector machine classifier. In: Proceedings of the international conference on multimedia computing and information technology–MCIT 2010, pp 93–96

    Google Scholar 

  108. Yan P, Bowyer KW (2005) ICP-based approaches for 3d ear recognition. In: Proceedings of SPIE 5779:282–291

    Google Scholar 

  109. Yan P, Bowyer KW (2005) Empirical evaluation of advanced ear biometrics. In: Proceedings of the IEEE computer vision and pattern recognition workshops–CVPRW 2005, vol 3, pp 41–48

    Google Scholar 

  110. Yan P, Bowyer KW (2005) Multi-biometrics 2D and 3D ear recognition. In: Kanade T, Jain A, Ratha NK (eds) Proceedings of the international conference on audio- and video-based person authentication–AVBPA 2005, LNCS 3546, pp 503–512

    Google Scholar 

  111. Yan P, Bowyer KW (2007) Biometric recognition using 3D ear shape. IEEE Trans Pattern Anal Mach Intell 29(8):1297–1308

    Article  Google Scholar 

  112. Yan P, Bowyer KW (2007) A fast algorithm for ICP-based 3D shape biometrics. Comput Vision Image Underst 107(3):195–202

    Article  Google Scholar 

  113. Yaqubi M, Faez K, Motamed S (2008) Ear recognition using features inspired by visual cortex and support vector machine technique. In: Proceedings of the international conference on computer and communication engineering–ICCCE 2008, pp 533–537

    Google Scholar 

  114. Yazdanpanah AP, Faez K, Amirfattahi R (2010) Multimodal biometric system using face, ear and gait biometrics. In: Proceedings of the 10th international conference on information science, signal processing and their applications–ISSPA 2010, pp 251–254

    Google Scholar 

  115. Yuan L, Zhichun Mu Z, Ying Liu Y (2006) Multimodal recognition using face profile and ear. In: Proceedings of the 1st international symposium on systems and control in aerospace and astronautics–ISSCAA 2006, pp 887–891

    Google Scholar 

  116. Yuan L, Mu Z-C (2007) Ear detection based on skin-color and contour information. In: Proceedings of the international conference on machine learning and cybernetics, vol 4, pp 2213–2217

    Google Scholar 

  117. Yuan L, Mu ZC (2007) Ear recognition based on 2D images. In: Proceedings of the 1st IEEE international conference on biometrics: theory, applications and systems–BTAS 2007, pp 1–5

    Google Scholar 

  118. Yuan L, Mu Z-C, Xu X-N (2007) Multimodal recognition based on face and ear. In: Proceedings of the international conference on wavelet analysis and pattern recognition–ICWAPR 2007, vol 3, pp 1203–1207

    Google Scholar 

  119. Yuan L, Zhang F (2009) Ear detection based on improved AdaBoost algorithm. In: Proceedings of the international conference on machine learning and cybernetics, vol 4, pp 2414–2417

    Google Scholar 

  120. Yuan L, Mu Z-C (2012) Ear recognition based on local information fusion. Pattern Recogn Lett 33:182–190

    Google Scholar 

  121. Zeng H, Mu Z-C, Wang K, Sun C (2009) Automatic 3D ear reconstruction based on binocular stereo vision. In: Proceedings of the IEEE international conference on systems, man and cybernetics–SMC 2009, pp 5205–5208

    Google Scholar 

  122. Zeng H, Mu Z-C, Yuan L, Wang S (2009) Ear recognition based on the SIFT descriptor with global context and the projective invariants. In: Proceedings of the 5th international conference on image and graphics–ICIG 2009, pp 973–977

    Google Scholar 

  123. Zhang D, Lu G (2002) Shape-based image retrieval using generic Fourier descriptor. Sig Process Image Commun 17(10):825–848

    Google Scholar 

  124. Zhang H-J, Mu Z-C, Qu W, Liu L-M, Zhang C-Y (2005) A novel approach for ear recognition based on ICA and RBF network. In: Proceedings of the 2005 international conference on machine learning and cybernetics, vol 7, pp 4511–4515

    Google Scholar 

  125. Zhang H, Mu Z (2008) Compound structure classifier system for ear recognition. In: Proceedings of the IEEE international conference on automation and logistics - ICAL 2008, pp 2306–2309

    Google Scholar 

  126. Zhang Z, Liu H (2008) Multi-view ear recognition based on B-Spline pose manifold construction. In: Proceedings of the 7th world congress on intelligent control and automation - WCICA 2008, pp 2416–2421

    Google Scholar 

  127. Zhou J, Cadavid S, Abdel-Mottaleb M (2010) Histograms of categorized shapes for 3D ear detection. In: Proceedings of the 4th IEEE international conference on biometrics: theory applications and systems - BTAS 2010, pp 1–6

    Google Scholar 

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Authors and Affiliations

  1. University of Cagliari, Via Ospedale, 72 - 09124, Cagliari, Italy

    Silvio Barra

  2. Sapienza University of Rome, Via Salaria, 113 - 00198, Rome, Italy

    Maria De Marsico

  3. University of Salerno, Via Ponte Don Melillo, 84084, Fisciano, SA

    Michele Nappi

  4. University of Napoli Federico II, Via Cintia 21, 80126, Napoli, Italy

    Daniel Riccio

Authors
  1. Silvio Barra
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  2. Maria De Marsico
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  3. Michele Nappi
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  4. Daniel Riccio
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Correspondence to Maria De Marsico .

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Editors and Affiliations

  1. Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Jacob Scharcanski

  2. IT-Instituto de Telecomunicações, University of Beira Interior, Covilhã, Portugal

    Hugo Proença

  3. Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, USA

    Eliza Du

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Barra, S., De Marsico, M., Nappi, M., Riccio, D. (2014). Unconstrained Ear Processing: What is Possible and What Must Be Done. In: Scharcanski, J., Proença, H., Du, E. (eds) Signal and Image Processing for Biometrics. Lecture Notes in Electrical Engineering, vol 292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54080-6_6

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  • DOI: https://doi.org/10.1007/978-3-642-54080-6_6

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