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Gender Identification Using 2D Ear Biometric

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Intelligent Computing (SAI 2024)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 1018))

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Abstract

Due to the advancement of technologies, organizations, and governments increasingly rely on digital technologies to validate a person's identity for secure interactions and to provide services for digital access control. Critical transactions require accurate and reliable authentication and verification of a person to prevent fraud. The most reliable and secure digital technology is biometrics. In this work, we are analyzing ear biometrics. Previous studies in biometrics have shown that gender can be recognized by face and voice. In this paper, ear biometrics are used to recognize and identify genders. Machine learning and deep learning are explored for gender identification. Mask RCNN and Grabcut segmentation techniques are used for ear detection, and features are extracted from Histogram-Oriented Gradient and Gabor filters and classified by gender using machine learning and deep learning techniques. The machine and deep network models achieved recognition rates of 81.48% and 87.52% for the EarVN1.0 dataset, respectively. The proposed automated system is useful to recognize people using ear biometrics.

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References

  1. The world bank. https://id4d.worldbank.org/node/2096

  2. Srinivasan, L.: Automatic sketch generation for person recognition. In: IEEE International Conference on Signal and Image Processing (2023)

    Google Scholar 

  3. Srinivasan, L.: Frequency based gait gender identification. In: IEEE International Conference on Signal and Image Processing (2023)

    Google Scholar 

  4. Srinivasan, L.: Automatic gait gender classification using convolutional neural networks. In: ACM International Conference on Image Processing and Machine Vision, 13–15 January (2023)

    Google Scholar 

  5. Srinivasan, L.: Analysis of Gait Biometric in the frequency domain. In: IEEE Conference on Cloud Computing, Computer Vision and Image Processing (2022)

    Google Scholar 

  6. Unar, J., Seng, W.C., Abbasi, A.: A review of biometric technology along with trends and prospects. Pattern Recogn. 47, 2673–2688 (2014)

    Article  Google Scholar 

  7. Srinivasan, L.: Gait biometric for person re-identification. Int. J. Biomed. Biological Eng. 15(12), 326–330 (2021)

    Google Scholar 

  8. Song, L., Gong, D., Li, Z., Liu, C., Liu, W.: Occlusion robust face recognition based on mask learning with pairwise differential siamese network. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 27 October–2 November, pp. 773–78 (2019)

    Google Scholar 

  9. Li, P., Prieto, L., Mery, D., Flynn, P.J.: On low-resolution face recognition in the wild: comparisons and new techniques. IEEE Trans. Inf. Forensics Security 14, 2000–2012 (2019)

    Article  Google Scholar 

  10. https://www.colorado.edu/today/2019/10/08/facial-recognition-software-has-gender-problem

  11. Emersic, Z., Struc, V., Peer, P.: Ear recognition: More than a survey. Neurocomputing 255, 26–29 (2017)

    Article  Google Scholar 

  12. Abayomi-Alli, O., Misra, S., Abayomi-Alli, A., Odusami, M.: A review of soft techniques for SMS spam classification: methods, approaches and applications. Eng. Appl. Artif. Intell. 86, 197–212 (2019)

    Article  Google Scholar 

  13. Youbi, Z., Boubchir, L., Boukrouche, A.: Human ear recognition based on local multi-scale LBP features with city-block distance. Multi. Tools Appl. 78, 14425–14441 (2019)

    Google Scholar 

  14. Hassaballah, M., Alshazly, H., Abdelmgeid A.: Ear recognition using local binary patterns: A comparative experimental study. Expert Syst. Appl. 118, 182–200 (2019)

    Google Scholar 

  15. Klontz, J., Jain, A.: A case study on unconstrained facial recognition using the boston marathon bombings suspects. Computer 46(11) (2013)

    Google Scholar 

  16. Hoogstrate, A., Heuvel, H.V.D., Huyben, E.: Ear identification based on surveillance camera images. Sci. Justice 41(3), 167–172 (2001)

    Article  Google Scholar 

  17. Gutierrez, L., Melin, P., Lopez, M.: Modular neural network integrator for human recognition from ear images. In: International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain, pp. 1–5 (2010)

    Google Scholar 

  18. Burge, M., Burger, W.: Ear biometrics. Biometrics: Personal Identification in Networked Society, Kluwer Academic, Dordrecht, pp. 273–286 (1998)

    Google Scholar 

  19. Burge, M., and Burger, W.: Ear biometrics in computer vision. In: Proc. International Conference on Pattern Recognition, pp. 822–826 (2000)

    Google Scholar 

  20. Hurley, D.J., Nixon, M.S., Carter, J.N.: Force field energy functionals for image feature extraction. In: Proceedings of 10th British Machine Vision Conference, pp. 604–613 (1999)

    Google Scholar 

  21. Abdel-Mottaleb, M., Zhou, J.: Human ear recognition from face profile images. In: International Conference on Biometrics, pp. 786–792 (2006)

    Google Scholar 

  22. Khorsandi, R., Abdel-Mottaleb, M.: Gender classification using 2-D ear images and sparse representation. In: IEEE Workshop on Applications of Computer Vision, pp. 461–466 (2013)

    Google Scholar 

  23. Yaman, D., Eyiokur, F.I., Sezgin, N, Ekenel, H.K.: Age and gender classification from ear images. In: International Workshop on Biometrics and Forensics, pp. 1–7 (2018)

    Google Scholar 

  24. Lei, J., Zhou, J., Abdel-Mottaleb, M.: Gender classification using automatically detected and aligned 3D ear range data. In: IEEE International Conference on Biometrics, pp. 1–7 (2013)

    Google Scholar 

  25. Yaman, D., Irem Eyiokur, F., Kemal Ekenel, H.: Multimodal age and gender classification using ear and profile face images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop (2019)

    Google Scholar 

  26. Yaman, D., Eyiokur, F.I., Sezgin, N. Ekenel, H.K.: Age and gender classification from ear images. In: IEEE International Workshop on Biometrics and Forensic, pp. 1–7 (2018)

    Google Scholar 

  27. Attarchi, S., Faez, K., Rafiei, A.: A new segmentation approach for ear recognition. Advanced Concepts for Intelligent Vision Systems. Lecture Notes in Computer Science, vol 5259, pp. 1030–1037 (2008)

    Google Scholar 

  28. Yuan, L., Mu, Z., Liu, Y.: Multimodel recognition using face profile and ear. In: 1st International Symposium on Systems and Control in Aerospace and Astronautics, pp. 887–991 (2006)

    Google Scholar 

  29. https://www.makesense.ai/

  30. Rother, C., Kolmogorov,V., Blake, A.: GrabCut: interactive foreground extraction using iterated graph cut. ACM Trans. Graph. 23(3) (2004)

    Google Scholar 

  31. Chang, K., Bowyer, K.W., Sarkar, S., Victor, B.: Comparison and combination of ear and face images in appearance-based biometrics. IEEE Trans. 25(9), 1160–1165 (2003)

    Google Scholar 

  32. Iwano, K., Hirose,T., Kamibayashi, E., Furui, S.: Audio-visual person authentication using speech and ear images. In: Proc. of Workshop on Multimodal User Authentication, pp. 85–90 (2003)

    Google Scholar 

  33. Victor, B., Bowyer, K.W., Sarkar, S.: An evaluation of face and ear biometrics. In: Proc. ICPR, pp. 429–432 (2002)

    Google Scholar 

  34. Wang, H., Turusawa, K., Nakayama, S.: Study on Human Recognition with Ear Image. In: Information Processing Society of Japan (IPSJ) Kyushu Chapter Symposium (2003)

    Google Scholar 

  35. Srinivasan, L.: Identification and classification of COVID Images using Machine Learning. In: International Conference on AI in Aging and Age-related Diseases. Elsevier (2022)

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of West London, London, UK

    Lavanya Srinivasan

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  1. Lavanya Srinivasan
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Correspondence to Lavanya Srinivasan .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Srinivasan, L. (2024). Gender Identification Using 2D Ear Biometric. In: Arai, K. (eds) Intelligent Computing. SAI 2024. Lecture Notes in Networks and Systems, vol 1018. Springer, Cham. https://doi.org/10.1007/978-3-031-62269-4_30

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