A modal for better authentication using hybrid biometrics by adding a side face with an ear
Main Article Content
Abstract
Customer support in digital environment is most important by facilitating secure authentication and verification for both customer and retail stores. Ear and side face as hybrid biometric is effective and efficient method for authentication and recognition in biometrics. Many researchers reported that ear can be a biometric trait and has sufficient potential to be considered as biometric characteristic and same for the face it has been proved. In this research paper we investigated, with help of MATLAB tool. Idea of multiple traits of ear and side face biometric technique in retail stores for authorization and identification of customers in digital environment which is need of today.
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
References
Arbab-Zavar, B., and Nixon, M., Robust log-Gabor
filter for ear biometrics. In: 19th International
Conference on Pattern Recognition, pp. 1–4, 2008.
https://doi.org/10.1109/ICPR.2008. 4761843.
Arbab-Zavar, B., Nixon, M., and Hurley, D., On modelbased analysis of ear biometrics. In: First IEEE
International Confer- ence on Biometrics: Theory
Applications, and Systems, pp. 1–5, 2007.
https://doi.org/10.1109/BTAS.2007.4401937.
Attarchi, S., Faez, K., and Rafiei, A., A new
segmentation approach for ear recognition. In:
International Conference on Advanced Concepts
for Intelligent Vision Systems, pp. 1030– 1037,
https://doi.org/10.1007/978-3-540-88458-3
Badrinath, G., and Gupta, P., Feature level fused ear
biometric system. In: Seventh International
Conference on Advances in Pattern Recognition
(ICPR), pp. 197–200, 2009.
https://doi.org/10.1109/ICAPR.2009.27.
Basit, A., and Shoaib, M., A human ear recognition
method using nonlinear curvelet feature subspace.
Int. J. Comput. Math. 91(3):616–624, 2014.
https://doi.org/10.1080/00207160.2013. 800194.
Benzaoui, A., Hadid, A., and Boukrouche, A., Ear
biometric recognition using local texture
descriptors. J. Electron. Imaging 23(5):053,008–
,008, 2014. https://doi.org/10.1117/
JEI.23.5.053008.
Benzaoui, A., Hezil, N., and Boukrouche, A., Identity
recognition based on the external shape of the
human ear. In: International Conference on Applied
Research in Computer Science and Engineering,pp. 1–5, 2015a. https://doi.org/10.1109/ARCSE.
7338129.
Bertillon, A., La photographic judiciaries: avec unappendicesur la classification et l’identification
anthropome´triques, 1890.
Burge, M., and Burger, W., Ear biometrics. Biometrics:
Personal Identification in Networked Society pp.
–286, https://doi.org/10.1007/0-306-47044-6
, 1996.
Burge, M., and Burger, W., Ear biometrics for machine
vision. In: 21st Workshop of the Austrian
Association for Pattern Recognition, pp. 275–282,
Burger, M., and Burger, W., Ear biometrics in computer
vision. In: International Conference on Pattern
Recognition, pp. 822– 826, 2000.
https://doi.org/10.1109/ICPR.2000.906202.
Bustard, J., and Nixon, M., Toward unconstrained ear
recognition from two-dimensional images. IEEE
Trans. Syst. Man Cybern. Syst. Hum. 40(3):486–
, 2010. https://doi.org/10.
/TSMCA.2010.2041652.
Chan, T. S., and Kumar, A., Reliable ear identification
using 2- D quadrature filters. Pattern Recogn. Lett.
(14):1870–1881, 2012.
https://doi.org/10.1016/j.patrec.2011.11.013.
Chang, K., Bowyer, K., Sarkar, S., and Victor, B.,
Comparison and combination of ear and face
images in appearance-based biometrics. IEEE
Trans. Pattern Anal. Mach. Intell. 25(9):1160–
, 2003.
https://doi.org/10.1109/TPAMI.2003.1227990.
Choras, M., Ear biometrics based on geometrical
feature extrac- tion. ELCVIA Electron. Lett.
Comput. Vision Image Anal. 5(3):84–95, 2005.
https://doi.org/10.5565/rev/elcvia.108.
Choras, M., Perspective methods of human
identification: ear biometrics. Opto-Electron. Rev.
(1):85–96, 2008. https://doi.org/10.2478/s11772-
-0033-5.
Choras, M., and Choras, R., Geometrical algorithms of
ear con- tour shape representation and feature
extraction. In: Sixth Inter- national Conference on
Intelligent Systems Design and Applica- tions
(ISDA), Vol. 2, pp. 451-456, 2006.
https://doi.org/10.1109/ ISDA.2006.253879.
De Marsico, M., Michele, N., and Riccio, D., HERO:
human ear recognition against occlusions. In: IEEE
Computer Society Conference on Computer
Vision and Pat- tern Recognition Workshops
(CVPRW), pp. 178–183, 2010.
https://doi.org/10.1109/CVPRW.2010.5544623.
Dewi, K., and Yahagi, T., Ear photo recognition using
scale invariant key points. In: Proceedings of the
Second IASTED Inter- national Conference on
Computational Intelligence, pp. 253– 258, 2006.
Guo, Y., and Xu, Z., Ear recognition using a new local
matching approach. In: 15th IEEE International
Conference on Image Processing, pp. 289–292,
https://doi.org/10.1109/ ICIP.2008.4711748.
Hai-Long, Z., and Zhi-Chun, M., Combining wavelet
trans- form and orthogonal centroid algorithm for
ear recognition. In: 2nd IEEE International
Conference on Computer Science and Information
Technology (ICCSIT), pp. 228–231, 2009.
https://doi.org/10.1109/ICCSIT.2009.5234392.
Houcine, B., Hakim, D., Amir, B., and Hani, B., Ear
recognition based on multi-bags-of features
histogram. In: 3Rd International Conference on
Control, Engineering & Information Technology
(CEIT), Vol. 2015, pp. 1–6, 2015.
https://doi.org/10.1109/CEIT.2015.7232997.
Hurley, D. J., Nixon, M., and Carter, J., Force field
energy functional for image feature extraction.
Image Vis. Comput. 20(5-6):311–317, 2002.
https://doi.org/10.1016/S0262- 8856(02)00003-3.
Iannarelli, A., Ear identification Paramont Publishing,
ISBN: Paramont Publishing, 1989.
Kisku, D., Mehrotra, H., Gupta, P., and Sing, J., SIFTbased ear recognition by fusion of detected
keypoints from color similarity slice regions. In:
International Conference on Advances in
Computational Tools for Engineering Applications,
pp. 380–385, 2009.
https://doi.org/10.1109/ACTEA.2009.5227958.
Kumar, A., and Wu, C., Automated human
identification using ear imaging. Pattern Recogn.
(3):956–968, 2012.
https://doi.org/10.1016/j.patcog.2011.06.005.
Kumar, A., and Zhang, D., Ear authentication using logGabor wavelets. Defense and Security Symposium
pp. 65,390A– 65,390A,
https://doi.org/10.1117/12.720244, 2007.
Kumar, A., Hanmandlu, M., Kuldeep, M., and Gupta,
H., Auto- matic ear detection for online biometric
applications. In: Third National Conference on
Computer Vision, Pattern Recognition, Image
Processing and Graphics (NCVPRIPG), pp. 146–
, 2011.
https://doi.org/10.1109/NCVPRIPG.2011.69.
Meraoumia, A., Chitroub, S., and Bouridane, A., An
auto- mated ear identification system using Gabor
filter responses. In: 13th International Conference
on New Circuits and Systems (NEWCAS), pp. 1–4,
https://doi.org/10.1109/NEWCAS.
7182085.
Moreno, B., Sanchez, A., and Ve´lez, J., On the
use of outer ear images for personal identification
in security applications. In: 33rd Annual
International Carnahan Conference on Security
Technology, pp. 469–476, 1999.
https://doi.org/10.1109/CCST. 1999.797956.
Mu, Z., Yuan, L., Xu, Z., Xi, D., and Qi, S., Shape and
structural feature based ear recognition. In:
Advances in Biometric Person Authentication, pp.
–670, 2004. https://doi.org/10.1007/978- 3-540-
-4 76.
Nanni, L., and Lumini, A., Fusion of color spaces for
ear authentication. Pattern Recogn. Lett.
(9):1906–1913, 2009.
https://doi.org/10.1016/j.patcog.2008.10.016.
Pflug, A., Busch, C., and Ross, A., 2D ear
classification based on unsupervised clustering. In:
IEEE International Joint Conference on
Biometrics (IJCB), pp. 1–8, 2014a.
https://doi.org/10.1109/BTAS.2014.6996239.
Pflug, A., Paul, P., and Busch, C., A comparative study
on texture and surface descriptors for ear
biometrics. In: International Carnahan Conference
on Security Technology (ICCST), pp. 1–6, 2014b.
https://.
Prakash, S., and Gupta, P., An efficient ear recognition
technique invariant to illumination and pose.
Telecommun. Syst. 52(3):1435–1448, 2013.
https://doi.org/10.1007/s11235- 011-9621-2.
Rahman, M., Islam, M. R., Bhuiyan, N., Ahmed, B.,
and Islam, A., Person identification using ear
biometrics. Int. J. Comput. Integr. Manuf. 15(2):1–
, 2007.
Sana, A., Gupta, P., and Purkait, R., Ear
biometrics: a new approach. Advances in Pattern
Recognition pp. 46–50,
https://doi.org/10.1142/9789812772381 0006,
Victor, B., Bowyer, K., and Sarkar, S., An evaluation of
face and ear biometrics. In: 16Th International
Conference on Pattern Recognition, Vol. 1, pp.
–432, 2002. https://doi.org/10.1109/
ICPR.2002.1044746.
Xiaoyun, W., and Weiqi, Y., Human ear recognition
based on block segmentation. In: International
Conference on Cyber-Enabled Distributed
Computing and Knowledge Discovery, pp. 262–
, 2009.
https://doi.org/10.1109/CYBERC.2009.5342143.
Zhang, H. J., Mu, Z. C., Qu, W., Liu, L. M., and
Zhang, C. Y., A novel approach for ear recognition
based on ICA and RBF network. In: 2005
International Conference on Machine Learning and
Cybernetics, Vol. 7, pp. 4511–4515, 2005.
https://doi.org/10.1109/ICMLC.2005.1527733.
Zhou, Y., and Zaferiou, S., Deformable models of ears
in-the- wild for alignment and recognition. In:
Th IEEE International Conference on Automatic
Face & Gesture Recognition (FG 2017), pp. 626–
, 2017. https://doi.org/10.1109/FG.2017.79.
An introduction to Biometric Recognition, Anil K.Jain,
Arun Ross, Salil Prabhakar, IEEE Transactions on
Circuits And System For Video Technology, Vol
, No. 1 Jan-2004
https://en.wikipedia.org/wiki/Facial_recognition_syste
m , The History of Information Security, 1st
Edition
A.S. Tolba, A.H. EL-Baz and A.A. El-Harby, Face
Recognition: A Literature Review, International
Journal of Signal Processing 2;2 2006,
doi=10.1.1.307.5530
Z. Liposcak and S. Loncaric, "A scale-space approach
to face recognition from profiles," in Proceedings
of the 8th International Conference on Computer
Analysis of Images and Patterns, Vol. 1689,
Lecture Notes In Computer Science. London, UK:
Springer- Verlag, 1999, pp.243-250 doi=
1007/3-540-48375-6_30
Multimodal Biometric Recognition using Human Ear
and Profile Face Partha Pratim Sarangi, B. S. P
Mishra†and Sachidanada Dehuri,School of
Computer Engineering, KIIT University,
Bhubaneswar, Odisha, India,Department of ICT,
FM University, Balasore, Odisha, India. 4th Int’l
Conf. on Recent Advances in Information
Technology | RAIT-2018
A Study on Human Recognition Using Auricle and Side
View Face Images Susan El-Naggar, Ayman Abaza
and Thirimachos Bourlai Springer International
Publishing AG 2018 P. Karampelas and T. Bourlai
(eds.), Surveillance in Action, Advanced Sciences
and Technologies for Security Applications,
https://doi.org/10.1007/978-3-319-68533-5_4
A Multimodal Biometric Authentication System Using
Ear and Face Mostafa Akhavansaffar *, Ali
Nakhaei , Mostafa Mokhtari Ardakan Department
of ICT Engineering, Payame Noor
Universtiy(PNU), Tehran, I. R of Iran Manuscript
submitted August 2, 2017; accepted November 5,
doi: 10.17706/jcp.13.7. 876-888
Learning Pose-Aware Models for Pose-Invariant Face
Recognition in the Wild Iacopo Masi, Feng-ju
Chan, Jongmoo Choi, Shai Harel, Jungyeon Kim,
KangGeon Kim, Jatuporn Leksut, Stephen Rawls,
Yue Wu, Tal Hassner, Wael AbdAlmageed, Gerard
Medioni, Louis-Philippe Morency, Prem Natarajan,
Ram Nevatia DOI 10.1109/TPAMI.2018.2792452,
IEEE Transactions on Pattern Analysis and
Machine Intelligence.
HUMAN FACE PROFILE RECOGNITION BY
COMPUTER CHYUAN JY Wu and JUN S.
HUANG* Pattern Recognition, Vol. 23, No. 3~4,
pp. 255 259, 1990 Printed in Great Britain,
Side-View Facial Recognition: Major Issue in Face
Recognition, Harshit Patel1, Ria Yadav2 1, 2 B.
Tech (CSE), 4thYear (1503510033), BBDIT
Ghaziabad (035), Dr. APJ Abdul Kalam Technical
University, International Journal for Research in
Applied Science & Engineering Technology
(IJRASET) Volume 6 Issue XI, Nov 2018
MULTIMODAL RECOGNITION BASED ON FACE
AND EAR,LI YUAN, ZHI-CHUN MU, XIAONA XU, School of Information Engineering,
University of Science and Technology Beijing,
Beijing, 100083, China,E-MAIL:
yuanli64@hotmail.com, Proceedings of the 2007
International Conference on Wavelet Analysis and
Pattern Recognition, Beijing, China, 2-4 Nov. 2007
Multimodal biometrics system based on face profile and
ear Iman S. Youssef Ayman A. Abaza Mohamed E.
Rasmy and Ahmed M. Badawi ,Systems and
Biomedical Engineering, Cairo University, Egypt;
West Virginia High Tech Foundation, Fairmont,
USA.