UPSI Digital Repository (UDRep)
|
|
|
Abstract : Universiti Pendidikan Sultan Idris |
Human ear is an intriguing individual anatomical part of a passive, physiological biometrics systems based on the image acquired from digital cameras. The human ear has many singular features that permit the finding of particular individuals. It could be implemented as effective biometrics systems, for example, in crowd surveillance and identifying terrorist at public places such as airports, as well as controlling access to government offices. Challenges and issues faced in the field of human identification using ear detection and recognition includes problem of occlusion, varieties in illumination, and real-time implementation in accessing information from an integrated database system with higher accuracy. Research on ear detection and recognition systems have been developing in a steady rate and mostly are constrained to controlled indoor environment. Notwithstanding, other research issues incorporate; ear print criminology; ear symmetry; and ear uniqueness. This paper presents a review of existing biometric system based on ear features and proposes a novel hybrid ear recognition framework for the advancement of passive human identification technology. The aim of this work is to build a passive identification system for hybrid ear biometric from digital image database that is collected from two types of identifiers (right-left ear of the same person). |
References |
Abaza, A., & Ross, A. (2010). Towards understanding the symmetry of human ears: A biometric perspective. IEEE 4th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2010. https://doi.org/10.1109/BTAS.2010.5634535 Alemran, A., & Rahmatullah, B. (2019). Novel Hybrid Ear Recognition Framework in Passive Human Identification. International Journal of Academic Research in Business and Social Sciences, 9(14), 1–8. Anwar, A. S., Ghany, K. K. A., & Elmahdy, H. (2015). Human Ear Recognition Using Geometrical Features Extraction. Procedia Computer Science, 65(Iccmit), 529–537. https://doi.org/10.1016/j.procs.2015.09.126 Arbab-Zavar, B., Nixon, M. S., & Hurley, D. J. (2007). On Model-Based Analysis of Ear Biometrics. 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems. https://doi.org/10.1109/BTAS.2007.4401937 Arbab-Zavar, Banafshe, & Nixon, M. S. (2011). On guided model-based analysis for ear biometrics. Computer Vision and Image Understanding, 115(4), 487–502. https://doi.org/10.1016/j.cviu.2010.11.014 Burge, M., & Burger, W. (1998). Using Ear Biometrics for Passive Identification. 14th IIternational Conference on Information Security, 98, 139–148. Burge, M., & Burger, W. (2000). Ear biometrics in computer vision. Proceedings 15th International Conference on Pattern Recognition. Los Alarnitos, CA, USA: IEEE Computer Society., 2, 822–826 Chen, L., & Mu, Z. (2016). Partial Data Ear Recognition From One Sample per Person Long. Ieee Transactions on Human-Machine Systems, 1–11. Chen, L., Mu, Z., Nan, B., Zhang, Y., & Yang, R. (2017). TDSIFT: a new descriptor for 2D and 3D ear recognition. Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 10225(Icgip 2016), 102250C. https://doi.org/10.1117/12.2266727 Chora?, M. (2005). Ear Biometrics Based on Geometrical Feature Extraction. Electronic Letters on Computer Vision and Image Analysis, 5(3), 84–95. https://doi.org/10.1007/978-3-540-30074- 8_7 Lammi, H. (2004). Ear biometrics. Tech. Rep. Lappeenranta University of Technology., 1–6. Lu, L., Xiaoxun, Z., Youdong, Z., & Yunde, J. (2006). Ear Recognition Based on Statistical Shape Model. First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC’06), 3. https://doi.org/10.1109/ICICIC.2006.445 Mu, Z. (2007). Ear Recognition based on 2D Images. 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems, (1), 1–5. https://doi.org/10.1109/BTAS.2007.4401941 Omara, I., Li, F., Zhang, H., & Zuo, W. (2016). A novel geometric feature extraction method for ear recognition. Expert Systems with Applications, 65, 127–135. https://doi.org/10.1016/j.eswa.2016.08.035 Prakash, S., & Gupta, P. (2012). An efficient ear localization technique. Image and Vision Computing, 30(1), 38–50. https://doi.org/10.1016/j.imavis.2011.11.005 Prakash, S., Prakash, S., Jayaraman, U., Jayaraman, U., Gupta, P., & Gupta, P. (2009). A Skin-Color and Template Based Technique for Automatic Ear Detection. Icapr, 213–216. https://doi.org/10.1109/ICAPR.2009.31 Saranya, M., Cyril, G. L. I., & Santhosh, R. R. (2016). An approach towards ear feature extraction for human identification. International Conference on Electrical, Electronics, and Optimization Techniques, ICEEOT 2016, 4824–4828. https://doi.org/10.1109/ICEEOT.2016.7755636 Shih, H. C., Ho, C. C., Chang, H. T., & Wu, C. S. (2009). Ear detection based on arc-masking extraction and AdaBoost polling verification. IIH-MSP 2009 - 2009 5th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 669–672. https://doi.org/10.1109/IIH-MSP.2009.75 Surya Prakash, U. J. and P. G. (2008). Ear Localizatilon from Side Face images using Distance Transform and Template Matching. Image (Rochester, N.Y.), (c). Wahab, N. K. A., Hemayed, E. E., & Fayek, M. B. (2012). HEARD: An automatic human EAR detection technique. International Conference on Engineering and Technology, ICET 2012 - Conference Booklet. https://doi.org/10.1109/ICEngTechnol.2012.6396118 Xiaoxun, Z., & Yunde, J. (2007). Symmetrical null space LDA for face and ear recognition. Neurocomputing, 70(4–6), 842–848. https://doi.org/10.1016/j.neucom.2006.10.016 Yuan, L., & Mu, Z. C. (2012). Ear recognition based on local information fusion. Pattern Recognition Letters, 33(2), 182–190. https://doi.org/10.1016/j.patrec.2011.09.041
|
This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |