Manuscript Number : CSEIT172538
Ear Recognition using Scale Invarient Feature Transform (SIFT)
Authors(2) :-Payal Verma, Sandeep Patil
Biometric-based solutions are able to provide for confidential financial transactions and personal data privacy. The need for biometrics can be found in federal, state and local governments, in the military, and in commercial applications. Enterprise-wide network security infrastructures, government IDs, secure electronic banking, investing and other financial transactions, retail sales, law enforcement, and health and social services are already benefiting from these technologies. There exist few techniques in the literature which can be used to detect ear auto-matically. A detailed review of these techniques is as follows. The first well known technique for ear detection is due to Burge and Burger . It has detected ears with the help of deformable contours. But contour initialization in this technique needs user interaction. As a result, ear localization is not fully automatic. Hurley et al.  have used force field technique to get the ear location. The technique claims that it does not require exact ear localization for ear recognition. However, it is only applicable when a small background is present in ear image. In , Yan and Bowyer have used manual technique based on two-line landmark to detect ear where one line is taken along the border between the ear and the face while other line is considered from the top of the ear to the bottom. The 2D ear localization technique proposed by Alvarez et al.  uses ovoid and active contour (snake)  models. Ear boundary is estimated by fitting the contour of an ear in the image by combining snake and ovoid models. This technique requires an initial approximated ear contour to execute and hence cannot be used in fully automated ear recognition system. There is no empirical evaluation of the technique.
Financial Transactions, Personal Data Privacy, 2D Ear Localization Technique, Ear Detection, Difference of Gaussian
- AlaaTharwat, Abdelhameed Ibrahim, Aboul Ella Hassanien and Gerald Schaefer "Ear Recognition Using Block-Based Principal Component Analysis and Decision Fusion" 978-3-319-19941-2 24 , 2015, IEEE
- AsmaaSabet Anwar, Kareem Kamal A.Ghany, HeshamElmahdy "Human Ear Recognition Using Geometrical Features Extraction" phn: +2-012-210-78191; fax: +2-082-224-6896 ,Elsevier, 2015
- ShubhangiKhobragade ,DheerajDilipMor , AmanChhabra "A Method of Ear Feature Extraction for Ear Biometrics using MATLAB" 2015
- AsmaaSabetAnwar ,Kareem Kamal A.Ghany, HeshamElMahdy "Human Ear Recognition Using SIFT Features" 978-1-4673-9669-1,IEEE, 2015
- Peter Claes, Jonas Reijniers, Mark D. Shriver, JonatanSnyders, Paul Suetens, Joachim Nielandt, Guy De Tr and Dirk Vandermeulen "An investigation of matching symmetry in the human pinnae with possible implications for 3D ear recognition and sound localization" 10.1111/joa.12252 ,2014
- Dr.TamijeselvyPerumal, ShilpaSomasundar " Ear Recognition Using Kernel Based Algorithm"International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, April 2014
- N. B. Boodoo-Jahangeer and S. Baichoo "LBP-Based Ear Recognition" 978-1-4799-3163-7, IEEE, 2013
- Rajesh M Bodade , Sanjay N Talbar " Ear Recognition using Dual Tree Complex Wavelet Transform " IJACSA,2013
- Fadi N. Sibai ,AmnaNuaimi , AmnaMaamari , RashaKuwair "Ear recognition with feed-forward artificial neural networks" DOI 10.1007/s00521-012-1068-1, SPRINGER, JULY 2012
- SuryaPrakash , Phalguni Gupta "An efficient ear recognition technique invariant to illumination and pose"DOI 10.1007/s11235-011-9621-2 , Springer Science, 2011
- Li Yuan ,Zhichun Mu "Ear recognition based on local information fusion"Elsevier, 2011
- Syed M.S. Islam , Rowan Davies , Mohammed Bennamoun , Ajmal S. Mian "Efficient Detection and Recognition of 3D Ears"DOI 10.1007/s11263-011-0436-0, Springer Science, 2011
- S. M. S. Islam, M. Bennamoun and R. Davies "Fast and Fully Automatic Ear Detection Using Cascaded AdaBoost" Elsevier,2008.
- M. Ali , M. Y. Javed and A. Basit "Ear Recognition Using Wavelets"Proceedings of Image and Vision Computing, December 2007.
- Hui Chen and BirBhanu "Human Ear Recognition in 3D" IEEE ,2007
- Hui Chen and BirBhanu "Shape Model-Based 3D Ear Detection from Side Face Range Images"Computer Society Conference on Computer Vision and Pattern Recognition, 2005 IEEE
- Hui Chen and BirBhanu "Contour Matching for 3D Ear Recognition"0-7695-2271-8/05 , IEEE 2005
- Zhichun Mu, Li Yuan, ZhengguangXu, Dechun Xi, and Shuai Qi "Shape and Structural Feature Based Ear Recognition"IJSETR,2004
- Burge, Mark and Burger, Wilhelm 2000. Ear biometrics in computer vision. In Proceedings ofInternational Conference on Pattern Recognition (ICPR’00), vol. 2, 822-826.
- Hurley, David J., Mark S. Nixon, and John N. Carter. 2005. Force field feature extraction for ear biometrics. Computer Vision and Image Understanding 98(3): 491-512.
- Yan, Ping, Kelvin W. Bowyer. 2005. Empirical evaluation of advanced ear biometrics. In Proceedings of International Conference on Computer Vision and Pattern Recognition-Workshop,vol. 3, 41-48.
- Alvarez, L., E. Gonzalez and L. Mazorra. 2005. Fitting ear contour using an ovoid model. In Proceedings of IEEE International Carnahan Conference on Security Technology (ICCST’05),145-148.
Published in : Volume 2 | Issue 5 | September-October 2017
Date of Publication : 2017-10-31
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 189-192
Manuscript Number : CSEIT172538
Publisher : Technoscience Academy
URL : http://ijsrcseit.com/CSEIT172538