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An Expansive Study of Facial Approaches

V. Subha1 , M. Sahaya Pretha2

  1. Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India.
  2. Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India.

Section:Review Paper, Product Type: Journal Paper
Volume-6 , Issue-5 , Page no. 1165-1171, May-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i5.11651171

Online published on May 31, 2018

Copyright © V. Subha, M. Sahaya Pretha . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: V. Subha, M. Sahaya Pretha, “An Expansive Study of Facial Approaches,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1165-1171, 2018.

MLA Style Citation: V. Subha, M. Sahaya Pretha "An Expansive Study of Facial Approaches." International Journal of Computer Sciences and Engineering 6.5 (2018): 1165-1171.

APA Style Citation: V. Subha, M. Sahaya Pretha, (2018). An Expansive Study of Facial Approaches. International Journal of Computer Sciences and Engineering, 6(5), 1165-1171.

BibTex Style Citation:
@article{Subha_2018,
author = {V. Subha, M. Sahaya Pretha},
title = {An Expansive Study of Facial Approaches},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2018},
volume = {6},
Issue = {5},
month = {5},
year = {2018},
issn = {2347-2693},
pages = {1165-1171},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2126},
doi = {https://doi.org/10.26438/ijcse/v6i5.11651171}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i5.11651171}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2126
TI - An Expansive Study of Facial Approaches
T2 - International Journal of Computer Sciences and Engineering
AU - V. Subha, M. Sahaya Pretha
PY - 2018
DA - 2018/05/31
PB - IJCSE, Indore, INDIA
SP - 1165-1171
IS - 5
VL - 6
SN - 2347-2693
ER -

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Abstract

Biometrics is the study of human behavior and features using their biological patterns. Nowadays face recognition plays a leading role in biometric for identifying a person without human cooperation. It is most efficient and sophisticated security system. The face recognition system can be applied to a variety of applications, such as searching for a criminal record, searching for a particular crime, finding missing children based on a monitoring site and track crime detection in ATMs. There are two reliable biometric acknowledgment procedures, for example, unique mark and iris acknowledgment. In any case, these methods are meddling and their prosperity depends exceedingly on the client collaboration since the clients are requested to position their eye before the iris scanner or put their finger on the unique finger impression gadget keeping in mind the end goal to finish the procedure. This can be viewed as a convoluted procedure for a typical man. Then again, face recognition is non-meddlesome since it depends on pictures recorded by a removed camera and can be extremely compelling regardless of whether the client doesn`t know about the presence of the face recognition framework. In this paper, a comprehensive investigation of face detection and face recognition strategies together with face databases are delivered.

Key-Words / Index Term

Face detection, face recognition, face database, SVM, neural networks, SIFT

References

[1]. Bakhshi, Y., Kaur, S., & Verma, P. (2015). A Study based on Various Face Recognition Algorithms. International Journal of Computer Applications, 129(13), 16-20.
[2]. Bakshi, U., & Singhal, R. (2014). A survey on face detection methods and feature extraction techniques of face recognition. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 3(3), 233-237.
[3]. Nawaf Hazim Barnouti, (2016) “Face Recognition using PCA-BPNN with DCT Implemented on Face94 and Grimace Databases”, International Journal of Computer Applications 142(6):8-13.
[4]. Barnouti, N. H., Al-Dabbagh, S. S. M., Matti, W. E., & Naser, M. A. S. (2016). Face Detection and Recognition Using Viola-Jones with PCA-LDA and Square Euclidean Distance. International Journal of Advanced Computer Science and Applications (IJACSA), 7(5), 371-377.
[5]. Bedre, J. S., & Sapkal, S. (2012). Comparative Study of Face Recognition Techniques: A Review. Emerging Trends in Computer Science and Information Technology–2012 (ETCSIT2012) Proceedings published in International Journal of Computer Applications®(IJCA), 12.
[6]. Choi, J. Y., Ro, Y. M., & Plataniotis, K. N. (2012). Color local texture features for color face recognition. IEEE transactions on image processing, 21(3), 1366-1380.
[7]. De Carrera, P. F., & Marques, I. (2010). Face recognition algorithms. Master`s thesis in Computer Science, Universidad Euskal Herriko.
[8]. Draper, B. A., Baek, K., Bartlett, M. S., & Beveridge, J. R. (2003). Recognizing faces with PCA and ICA. Computer vision and image understanding, 91(1-2), 115-137.
[9]. Gollen, M. (2012). Comparative analysis of face recognition algorithms.
[10]. Gottumukkal, R., & Asari, V. K. (2004). An improved face recognition technique based on modular PCA approach. Pattern Recognition Letters, 25(4), 429-436.
[11]. Huang, G. B., Ramesh, M., Berg, T., & Learned-Miller, E. (2007). Labeled Faces in the wild: A database for studying face recognition in unconstrained environments (Vol. 1, No. 2, p. 3). Technical Report 07-49, University of Massachusetts, Amherst.
[12]. Jain, A. K., & Li, S. Z. (2011). Handbook of face recognition. New York: Springer.
[13]. Javed, M., & Gupta, B. (2013). Performance comparison of various face detection techniques. International Journal of Scientific Research Engineering & Technology (IJSRET) Volume, 2, 019-0027.
[14]. Jiang, N., Lu, Y., Tang, S., & Goto, S. (2010, December). Rapid face detection using a multi-mode cascade and separate haar feature. In Intelligent Signal Processing and Communication Systems (ISPACS), 2010 International Symposium on (pp. 1-4). IEEE.
[15]. Kadam, K. D. (2014). Face Recognition using Principal Component Analysis with DCT. International Journal of Engineering Research and General Science, ISSN, 2091-2730.
[16]. Kamerikar, U. A., & Chavan, M. S. (2014). Experimental Assessment of LDA and KLDA for Face Recognition. International Journal, 2(2).
[17]. Milborrow, S., Morkel, J., & Nicolls, F. (2010). The MUCT landmarked face database. Pattern Recognition Association of South Africa, 201(0).
[18]. Murtaza, M., Sharif, M., Raza, M., & Shah, J. (2014). Face recognition using adaptive margin fisher’s criterion and linear discriminant analysis. International Arab Journal of Information Technology, 11(2), 1-11.
[19]. Murtaza, M., Sharif, M., Raza, M., & Shah, J. (2014). Face recognition using adaptive margin fisher’s criterion and linear discriminant analysis. International Arab Journal of Information Technology, 11(2), 1-11.
[20]. Nagar, A., Nandakumar, K., & Jain, A. K. (2012). Multibiometric cryptosystems based on feature-level fusion. IEEE transactions on information forensics and security, 7(1), 255-268.
[21]. Nandini, M., Bhargavi, P., & Sekhar, G. R. (2013). Face recognition using neural networks. International Journal of Scientific and Research Publications, 3(3), 1.
[22]. Prof. B.S Patil, Prof. A.R Yardi, (2013), Real-time face recognition by varying number of eigenvalues, International Journal of Advanced Scientific and Technical Research Issue 3 volume 1, Jan-Feb 2013.
[23]. Rishiwal, V., & Gupta, A. (2012). Improved PCA algorithm for face recognition. World Applied Programming, 2(1), 55-59.
[24]. Senthilkumaran, N., & Rajesh, R. (2009). A study on edge detection methods for image segmentation. In Proceedings of the International Conference on Mathematics and Computer Science (ICMCS-2009) (Vol. 1, pp. 255-259).
[25]. Sharifara, A., Rahim, M. S. M., & Anisi, Y. (2014, August). A general review of human face detection including a study of neural networks and Haar feature-based cascade classifier in face detection. In Biometrics and Security Technologies (IS BEST), 2014 International Symposium on (pp. 73-78). IEEE.
[26]. Sodhi, K. S., & Lal, M. (2013). Comparative analysis of PCA-based face recognition system using different distance classifiers. Int. J. of Appl. or Innovation in Eng. & Manag, 2, 341-348.
[27]. Thomas Heseltine, (2012), Face Recognition: A Literature Review University of York, 2012
[28]. Zou, J., Ji, Q., & Nagy, G. (2007). A comparative study of local matching approach for face recognition. IEEE Transactions on image processing, 16(10), 2617-2628.
[29]. Zou, W. W., & Yuen, P. C. (2012). Very low-resolution face recognition problem. IEEE Transactions on Image Processing, 21(1),327-340.