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AEPI: insights into the potential of deep representations for human identification through outer ear images

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Abstract

For forensic human identification, one of the most important methods is by opting for protocols of biometrics. The outer human ear, also known as auricle or pinna, has unique characteristics, which are not even the same in identical twins. Thus, considering the uniqueness, reliability, and easy collectability of this trait, herein, a novel method is proposed for human identification namely Automated Ear Pinna Identification (AEPI). The proposed method opts for potentials of deep learning, by creating a unique blend of deep representation from a residual network and a spatial encoding block to identify human ear pinna imagery. The evaluation of the proposed method is also performed by comparing both, same and different ear pinna image pairs. Based on the evaluation, it is observed that the proposed method is 87.207% accurate for classification among classes of the dataset, 97.2% for gender-based classification and 99.0% accurate for identifying humans based on ear pinna images. These scores depict the strong potential and contribution of the proposed method in the field of ear biometrics and it is concluded that AEPI can aid the identification of humans based on ear pinna images in an accurate, effective and efficient manner. AEPI is freely available at (http://zeetu.org/AEPI.html).

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Data availability

Dataset used was a recently reported publicly available dataset, which can be downloaded at https://data.mendeley.com/datasets/yws3v3mwx3/4.

Code availability

Source code is available at: https://github.com/UsamaHasan/AEPI-Automated-Ear-Pinna-Identification. The standalone application is available at: http://zeetu.org/AEPI.html.

References

  1. Abaza A, Ross A, Hebert C, Harrison MAF, Nixon MS (2013) A survey on ear biometrics. ACM Comput Surv (CSUR) 45(2):1–35

    Article  Google Scholar 

  2. Abdelwhab A, Viriri S (2018) A survey on soft biometrics for human identification. Machine Learning and Biometrics, 1st Edition. InTechOpen, London

  3. Al Rahhal MM, Mekhalfi ML, Guermoui M, Othman E, Lei B, Mahmood A (2018) A dense phase descriptor for human ear recognition. IEEE Access 6:11883–11887

    Article  Google Scholar 

  4. Al Rahhal MM, Mekhalfi ML, Ali TAM, Bazi Y, Al Zuair M, Rangarajan L (2018) Ear recognition via sparse coding of local features. J Electron Imaging 27(1):013007

    Article  Google Scholar 

  5. Alkababji AM, Mohammed OH (2021) Real time ear recognition using deep learning. Telkomnika 19(2):523–530

    Article  Google Scholar 

  6. Benzaoui A, Hadid A, Boukrouche A (2014) Ear biometric recognition using local texture descriptors. J Electron Imaging 23(5):053008

    Article  Google Scholar 

  7. Cao K, Jain AK (2018) Automated latent fingerprint recognition. IEEE Trans Pattern Anal Mach Intell 41(4):788–800

    Article  Google Scholar 

  8. Chakraborti T, McCane B, Mills S, Pal U (2018) Loop descriptor: Local optimal-oriented pattern. IEEE Signal Process Lett 25(5):635–639

    Article  Google Scholar 

  9. Chatterjee A, Singh P, Bhatia V, Prakash S (2019) Ear biometrics recognition using laser biospeckled fringe projection profilometry. Opt Laser Technol 112:368–378

    Article  Google Scholar 

  10. Chen J, Shan S, He C, Zhao G, Pietikainen M, Chen X, Gao W (2009) WLD: A robust local image descriptor. IEEE Trans Pattern Anal Mach Intell 32(9):1705–1720

    Article  Google Scholar 

  11. Chowdhury DP, Bakshi S, Guo G, Sa PK (2018) On applicability of tunable filter bank based feature for ear biometrics: a study from constrained to unconstrained. J Med Syst 42(1):11

    Article  Google Scholar 

  12. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE 248–255

  13. Dodge S, Mounsef J, Karam L (2018) Unconstrained ear recognition using deep neural networks. IET Biom 7(3):207–214

    Article  Google Scholar 

  14. Doghmane H, Boukrouche A, Boubchir L (2019) A novel discriminant multiscale representation for ear recognition. Int J Biom 11(1):50–66

    Google Scholar 

  15. Dubey SR, Mukherjee S (2020) Ldop: local directional order pattern for robust face retrieval. Multimed Tools Appl 79(9):6363–6382

    Article  Google Scholar 

  16. Emeršič Ž, Štruc V, Peer P (2017) Ear recognition: More than a survey. Neurocomputing 255:26–39

    Article  Google Scholar 

  17. Fathi A, Naghsh-Nilchi AR (2012) Noise tolerant local binary pattern operator for efficient texture analysis. Pattern Recognit Lett 33(9):1093–1100

    Article  Google Scholar 

  18. Galdámez PL, Raveane W, Arrieta AG (2017) A brief review of the ear recognition process using deep neural networks. J Appl Log 24:62–70

    Article  MathSciNet  Google Scholar 

  19. Ghoualmi L, Draa A, Chikhi S (2016) An ear biometric system based on artificial bees and the scale invariant feature transform. Expert Syst Appl 57:49–61

    Article  Google Scholar 

  20. Gonzalez E (2018) AMI ear dataset accessed from https://ctim.ulpgc.es/research_works/ami_ear_database/.

  21. Hansley EE, Segundo MP, Sarkar S (2018) Employing fusion of learned and handcrafted features for unconstrained ear recognition. IET Biom 7(3):215–223

    Article  Google Scholar 

  22. Hassaballah M, Aly S (2015) Face recognition: challenges, achievements and future directions. IET Comput Vision 9(4):614–626

    Article  Google Scholar 

  23. Hassaballah M, Alshazly HA, Ali AA (2019) Ear recognition using local binary patterns: A comparative experimental study. Expert Syst Appl 118:182–200

    Article  Google Scholar 

  24. He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, pp 1026–1034

  25. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  26. Hezil N, Boukrouche A (2017) Multimodal biometric recognition using human ear and palmprint. IET Biom 6(5):351–359

    Article  Google Scholar 

  27. Hinton GE, Zemel RS (1994) Autoencoders, minimum description length and Helmholtz free energy. In: Advances in neural information processing systems, pp 3–10

  28. Hoang VT (2019) EarVN1. 0: A new large-scale ear images dataset in the wild. Data in brief 27

  29. Hussain W, Rasool N, Yaseen M (2020) ADVIT: Using the potentials of deep representations incorporated with grid-based features of dorsum vein patterns for human identification. Forensic Science International, 110345

  30. Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:150203167

  31. Jabid T, Kabir MH, Chae O (2010) Robust facial expression recognition based on local directional pattern. ETRI J 32(5):784–794

    Article  Google Scholar 

  32. Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:14126980

  33. Kumar A, Wu C (2012) Automated human identification using ear imaging. Pattern Recogn 45(3):956–968

    Article  Google Scholar 

  34. Kyrki V, Kamarainen J-K, Kälviäinen H (2004) Simple Gabor feature space for invariant object recognition. Pattern Recognit Lett 25(3):311–318

    Article  Google Scholar 

  35. Larrain T, Bernhard JS, Mery D, Bowyer KW (2017) Face recognition using sparse fingerprint classification algorithm. IEEE Trans Inf Forensics Secur 12(7):1646–1657

    Article  Google Scholar 

  36. LeCun Y, Bottou L, Bengio Y, Haffner P (1998)Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  37. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

  38. Lin M, Chen Q, Yan S (2013) Network in network. arXiv preprint arXiv:13124400

  39. Liu L, Fieguth P, Guo Y, Wang X, Pietikäinen M (2017) Local binary features for texture classification: Taxonomy and experimental study. Pattern Recogn 62:135–160

    Article  Google Scholar 

  40. Liu L, Chen J, Fieguth P, Zhao G, Chellappa R, Pietikäinen M (2019) From BoW to CNN: Two decades of texture representation for texture classification. Int J Comput Vision 127(1):74–109

    Article  Google Scholar 

  41. Nair V (2010) Hinton GE Rectified linear units improve restricted boltzmann machines. In: ICML

  42. Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in pytorch. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, USA

  43. Priyadharshini RA, Arivazhagan S, Arun M (2021) A deep learning approach for person identification using ear biometrics. Appl Intell 51(4):2161–2172

    Article  Google Scholar 

  44. Rasool N, Hussain W (2020) ForeStatistics: A windows-based feature-rich software program for performing statistics in Forensic DNA analysis, Paternity and relationship testing. Forensic Science International 307:110142

  45. Rifai S, Vincent P, Muller X, Glorot X, Bengio Y (2011) Contractive auto-encoders: Explicit invariance during feature extraction. In: Icml

  46. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556

  47. Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2016) Inception-v4, inception-resnet and the impact of residual connections on learning. arXiv preprint arXiv:160207261

  48. Vincent P, Larochelle H, Bengio Y, Manzagol P-A, Extracting (2008) and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on Machine learning, pp 1096-1103

  49. Wang L, Qian X, Zhang Y, Shen J, Cao X (2019) Enhancing sketch-based image retrieval by cnn semantic re-ranking. IEEE Trans Cybern 50(7):3330–3342

    Article  Google Scholar 

  50. Zhang Y, Mu Z, Yuan L, Yu C, Liu Q, USTB-Helloear(2017) : A large database of ear images photographed under uncontrolled conditions. In: International Conference on Image and Graphics. Springer, pp 405–416

  51. Zhang L et al (2021) Multimodal marketing intent analysis for effective targeted advertising. In: IEEE Trans Multimed 1-1. https://doi.org/10.1109/TMM.2021.3073267

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Authors and Affiliations

  1. National Center of Artificial Intelligence, Punjab University College of Information Technology, University of the Punjab, Lahore, Pakistan

    Usama Hasan

  2. Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan

    Waqar Hussain

  3. Center for Professional and Applied Studies, Lahore, Pakistan

    Nouman Rasool

Authors
  1. Usama Hasan
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  2. Waqar Hussain
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  3. Nouman Rasool
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Correspondence to Nouman Rasool.

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Hasan, U., Hussain, W. & Rasool, N. AEPI: insights into the potential of deep representations for human identification through outer ear images. Multimed Tools Appl 81, 10427–10443 (2022). https://doi.org/10.1007/s11042-022-12025-9

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  • DOI: https://doi.org/10.1007/s11042-022-12025-9

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