Home   >   CSC-OpenAccess Library   >    Manuscript Information
Face Recognition Using Neural Networks
Latha Pitchai p, L.Ganesan
Pages - 153 - 160     |    Revised - 30-10-2009     |    Published - 30-11-2009
Volume - 3   Issue - 5    |    Publication Date - November 2009  Table of Contents
MORE INFORMATION
KEYWORDS
Face recognition, Neural Classifier, Principal Component Analysis, Detection rate.
ABSTRACT
Abstract Face recognition is a form of computer vision that uses faces to identify a person or verify a person’s claimed identity. In this paper, a neural based algorithm is presented, to detect frontal views of faces. The dimensionality of input face image is reduced by the Principal component analysis and the Classification is by the neural back propagation network. This method is robust for a dataset of 300 face images and has better performance in terms of 80 – 90 % recognition rate.
CITED BY (56)  
1 Satonkar, S. S., Pathak, V. M., & Khanale, P. B. Face Recognition Using Principal Component Analysis and Artificial Neural Network of Facial Images Datasets in Soft Computing.
2 Olubunmi, A. J., Olusayo, O. E., Bola, A. A., & Ayodeji, O. O. Performance Evaluation Of Selected Principal Component Analysis-Based Techniques For Face Image Recognition.
3 Abukmeil, M. A., Elaydi, H., & Alhanjouri, M. (2015). Palmprint Recognitionvia Bandlet, Ridgelet, Wavelet and Neural Network. Journal of Computer Sciences and Applications, 3(2), 23-28.
4 Akkawar, M. A. B., & Burange, M. S. Review And Comparative Study of Face Recognition Using Different Neural Networking algorithm.
5 Mamankar, M. P. V., & Vyawahare, H. R. (2015). Design And Implementation Of Multiposes Face Recognization System.
6 Mousa, Y. N., & Abd-Alsalam, N. (2015). An Enhanced Empirical Method on Choosing the Highest Principal Features and the Number of Hidden Neurons in Principal Component Analysis-Artificial Neural Network Face Recognition based System. International Journal of Computer Applications, 111(14).
7 Jabarullah, B. M., & Babu, C. N. K. (2015). BPNN-hippoamy Algorithm for Statistical Features Classification. Indian Journal of Science and Technology, 8(14).
8 Toufiq, R., & Islam, M. R. (2014, April). Face recognition system using PCA-ANN technique with feature fusion method. In Electrical Engineering and Information & Communication Technology (ICEEICT), 2014 International Conference on (pp. 1-5). IEEE.
9 Aldhahab, A., Atia, G., & Mikhael, W. B. (2014, August). Supervised facial recognition based on multi-resolution analysis and feature alignment. In Circuits and Systems (MWSCAS), 2014 IEEE 57th International Midwest Symposium on (pp. 137-140). IEEE.
10 Mucha, C. M. (2014). Theorie und Empirie des Phonästhems (Doctoral dissertation, lmu).
11 Hijriah, A., Fauziyah, M., & Dewatama, D. (2014). Implementasi JST Backpropagation pada Face Recognition untuk Percepatan Proses Sistem Absensi. Jurnal Elektronika Otomasi Industri, 1(1).
12 Biswas, A., Ghose, M. K., & Pandit, M. (2014). Comparison of Different Neural Network Architectures for Classification of Feature Transformed Data for Face Recognition. International Journal of Computer Applications, 96(12).
13 Toufiq, R., Chowdhury, M. S. H., & Islam, M. R. (2014). Face Detection and Recognition System Using Back-Propagation Neural Network Classifier. Journal of Artificial Intelligence Research & Advances, 1(1), 21-26.
14 Tiwari, M. K., & Tripathi, N. Design and Implementation of a Novel Face Recognition System.
15 Husssain, L. (2014). Classification of Human Faces and Non Faces Using Machine Learning Techniques. Global Journal on Technology, (6).
16 Anita, C., & Khushbu, S.illumination invariant face recognition system.
17 Sharma, R., & Patterh, M. S. A Systematic Review of PCA and Its Different Form for Face Recognition.
18 Yadav, A., & Yadav, P. Face Recognition Techniques and Neural Network.
19 Abukmeil, M. A., Elaydi, H., & Alhanjouri, M. Palmprint Recognition by using Bandlet, Ridgelet, Wavelet and Neural Network.
20 SOFTWARE, Ivan T. T. (2013). UK Volchenko TG Yemel'yanenko Oles Honchar Dnipropetrovsk National University INFORMATION TECHNOLOGY AND SOFTWARE face recognition.
21 Volchenko, JK, & Yemel'yanenko, T. (2013). Information technology and software face recognition. Actual problems of automation and information technology (17), 52-58.
22 Panchal, K., & Shah, H. (2013, December). 3D Face Recognition Based on Pose Correction Using Euler Angle Method. In Machine Intelligence and Research Advancement (ICMIRA), 2013 International Conference on (pp. 467-471). IEEE.
23 Savchenko, Y. K., & Emelianenko, T. H. (2013).information technology and software face recognition. Actual problems of automation and information technology 17.
24 SOFTWARE, Ivan T. D. (2013). UK Vovchenko TG Emelianenko Oles Honchar Dnipropetrovsk National University INFORMATION TECHNOLOGY AND SOFTWARE face recognition.
25 Ghauri, S. A., Adeel, H., Abbas, S. S., Mir, A., & Nawaz, I. (2013). Face Recognition using Adaptive Neural Network. IJCCER, 1(4), 111-114.
26 Junoh, A. K., & Mansor, M. N. (2013). A Comprehensive Study of Crime Detection with PCA and Different Neural Network Approach. In Advances in Information Systems and Technologies (pp. 611-618). Springer Berlin Heidelberg.
27 Zeng, H., & Luo, R. (2013). Preferred Skin Color Enhancement of Digital Photographic Images. International Journal of Image Processing (IJIP), 7(4), 314.
28 Agarwal, P., & Prakash, N. (2013). An efficient back propagation neural network based face recognition system using haar wavelet transform and PCA. International Journal of Computer Science and Mobile Computing (IJCSMC), 2(5), 386-395.
29 Al-allaf, O. N., Tamimi, A. A., & Alia, M. A. (2013). Face Recognition System Based on Different Artificial Neural Networks Models and Training Algorithms. International Journal of Advanced Computer Science and Applications, 4(6).
30 Agarwal, P., & Prakash, N. (2013). Modular Approach for Face Recognition System using Multilevel Haar Wavelet Transform, Improved PCA and Enhanced Back Propagation Neural Network. International Journal of Computer Applications, 75(7), 29-36.
31 Ebied, H. M., Revett, K., & Tolba, M. F. (2013). Evaluation of unsupervised feature extraction neural networks for face recognition. Neural Computing and Applications, 22(6), 1211-1222.
32 Narang, G., Singh, S., & Narang, A. (2013, December). Robust face recognition method based on SIFT features using Levenberg-Marquardt Backpropagation neural networks. In Image and Signal Processing (CISP), 2013 6th International Congress on (Vol. 2, pp. 1000-1005). IEEE.
33 Debacq, S., De Roeck, S., & Roets, J. Project Machine Learning.
34 Pittalia, P. P., & Solanki, M. K. H. An Invention Approach to 3D Face Recognition using Combination of 2D Texture Data and 3D Shape Data.
35 Volchenko, Y. K., & Yemelianenko, T. H. (2013). INFORMATION TECHNOLOGY AND SOFTWARE face recognition. Actual problems of automation and information technology, 17.
36 Prakash, R. An Efficient Back Propagation Neural Network Based Face Recognition System Using Haar Wavelet Transform and PCA.
37 Shukla, S., Bhargav, A., & Badal, P. Review of Face Recognition Technology Using Feature Fusion Vector.
38 Alanizi, F. (2012).applying feature selection on local binary patterns/wldfor ethnicity classification for category-specific face recognition (doctoral dissertation, king saud university).
39 Hauser, V. (2012). Rozpoznávání obliceju v obraze (Doctoral dissertation, Vysoké ucení technické v Brne. Fakulta elektrotechniky a komunikacních technologií).
40 Paul, J. (2012). A New Reclassification Method for Highly Uncertain Microarray Data in Allergy Gene Prediction (Doctoral dissertation).
41 Khan, M. U., Habib, H. A., & Saleem, N. (2012). A Hybrid Approach for Gender Classification of Web Images. International Journal of Computer Applications, 54(7).
42 Rahman, M. U. (2012). A comparative study on face recognition techniques and neural network. arXiv preprint arXiv:1210.1916.
43 Achmad, B., & Firdausy, K. (2012). Neural Network-based Face Pose Tracking for Interactive Face Recognition System. International Journal on Advanced Science, Engineering and Information Technology, 2(1), 105-108.
44 Daramola, S. A., & Odeghe, O. S. (2012). Efficient face recognition system using artificial neural network. International Journal of Computer Applications, 41(21), 12-15.
45 Sawalha, R., & Doush, I. (2012). Face recognition using harmony search-based selected features. International Journal of Hybrid Information Technology, 5(2), 1-16.
46 Sehgal, U., & Saini, S.facial image recognition model implementation with artificial neural networks using dimensions reduction techniques pca.
47 Kumar, D. (2011). Harmony Search Algorithm for Feature Selection in Face Recognition. In Computational Intelligence and Information Technology (pp. 554-559). Springer Berlin Heidelberg.
48 Madan, D., & Srivastava, H. (2011). Finding faces for Gender classification using BPNN AND PCA based recognition (Doctoral dissertation).
49 Pilkington, D. (2011). Building a Robust Facial Recognition System Based on Generic Tools.
50 USLU, G. (2011). Personal identification through face recognition.
51 Mohamed, M. A., Abou-Elsoud, M. E., & Eid, M. M. (2011). Appearance-Based Automated Face Recognition System: Multi-Input Databases.
52 Jyoti, D., Chadha, A., Vaidya, P., & Roja, M. M. (2011). A robust, low-cost approach to Face Detection and Face Recognition. arXiv preprint arXiv:1111.1090.
53 Shermina, J., & Vasudevan, V. (2011). An efficient face recognition system based on the hybridization of invariant pose and illumination process. European Journal of Scientific Research, 64(2), 225-243.
54 Prasad, M. S. R. S., Panda, S. S., Deepthi, G., & Anisha, V. (2011). Face recognition using PCA and feed forward neural networks. International Journal of Computer Science and Telecommunications, 2(8).
55 Rahman, S., Naim, S. M., Farooq, A. A., & Islam, M. M. (2011). Performance of PCA based semi-supervised learning in face recognition using MPEG-7 edge histogram descriptor. Journal of Multimedia, 6(5), 404-415.
56 Pelican, E., & Grecu, L. (2010, May). Comparison between some matrix methods with applications in pattern recognition. In Applied Linear Algebra Conference, May (pp. 24-28).
1 Google Scholar 
2 ScientificCommons 
3 Academic Index 
4 CiteSeerX 
5 refSeek 
6 iSEEK 
7 Socol@r  
8 ResearchGATE 
9 Bielefeld Academic Search Engine (BASE) 
10 Scribd 
11 WorldCat 
12 SlideShare 
13 PDFCAST 
14 PdfSR 
Kailash J. Karande Sanjay N. Talbar “Independent Component Analysis of Edge Information for Face Recognition” International Journal of Image Processing Volume (3) : Issue (3) pp: 120 -131.
[1]. B.K.Gunturk,A.U.Batur, and Y.Altunbasak,(2003) “Eigenface-domain super-resolution for face recognition,” IEEE Transactions of . Image Processing. vol.12, no.5.pp. 597-606.
[2]. M.A.Turk and A.P.Petland, (1991) “Eigenfaces for Recognition,” Journal of Cognitive Neuroscience. vol. 3, pp.71-86.
[3]. T.Yahagi and H.Takano,(1994) “Face Recognition using neural networks with multiple combinations of categories,” International Journal of Electronics Information and Communication Engineering., vol.J77-D-II, no.11, pp.2151-2159.
[4]. S.Lawrence, C.L.Giles, A.C.Tsoi, and A.d.Back, (1993) “IEEE Transactions of Neural Networks. vol.8, no.1, pp.98-113.
[5]. C.M.Bishop,(1995) “Neural Networks for Pattern Recognition” London, U.K.:Oxford University Press.
Associate Professor Latha Pitchai p
- India
plathamuthuraj@gmail.com
Dr. L.Ganesan
Government - India


CREATE AUTHOR ACCOUNT
 
LAUNCH YOUR SPECIAL ISSUE
View all special issues >>
 
PUBLICATION VIDEOS