Authors: Shakir F. Kak1 & Firas Mahmood Mustafa2 & Pedro Valente3
1Duhok Polytechnic University (DPU), Akre Technical Institute, Iraq
2Duhok Polytechnic University (DPU), Iraq
3Universidade Portucalense (UPT), Research on Economics, Management, and Information, Porto, Portugal
Abstract: Face recognition has become an attractive field in computer-based application development in the last few decades. This is because of the wide range of areas in which it is used. In addition, because of the wide variations of faces, face recognition from database images, real data, capture images, and sensor images are a challenging problem and limitation. Image processing, pattern recognition, and computer vision are relevant subjects to the face recognition field. The innovation of new approaches of face authentication technologies is a continuous subject to building much stronger face recognition algorithms. In this work, to identify a face, three major strategies for feature extractions are discussed. Appearance-based, Model-based methods and hybrid methods as feature extractions techniques are discussed too. There is also a review of major person recognition research. The characteristics of good face authentication applications, Classification, Distance measurements, and face databases are discussed while the final suggested methods are presented. This research has six sections ordered as follows: Section one is the introduction. Section two is dedicated to applications related to face recognition. In section three, face recognition techniques are presented by details. Then, classification types are illustrated in section four. In section five, standard face databases are presented. Finally, in section six, the conclusion is presented followed by the list of references.
Keywords: Appearance-Based Model, Model-Based, Hybrid Based, Classification, Distance Measurements, Face Databases, Face Recognition
Download the PDF Document from here.
Abdullah, M., Wazzan, M., & Bo-saeed, S. (2012, March). Optimizing face recognition using PCA. International Journal of Artificial Intelligence & Applications, 3(2), 23-31.
Adriansyah, A., & Dani, A. W. (2014). Design of Small Smart Home System Based on Arduino. Electrical Power, Electronics, Communications, Controls, and Informatics Seminar (EECCIS) (pp. 121-125). Malang, Indonesia: IEEE.
Akrouf, S., Sehili, M. A., Chakhchoukh, A., Mostefai, M., & Youssef, C. (2014, June-July). Face Recognition using Principal Component Analysis with DCT. International Journal of Engineering Research and General Science, 2(4), 276-280.
Al-Allaf, O. N. (2014, February). Review of face detection systems based artificial neural networks algorithms. The International Journal of Multimedia & Its Applications, 6(1), 1-16.
Barnouti, N. H. (2016, May). Face Recognition using PCA-BPNN with DCT Implemented on Face94 and Grimace Databases. International Journal of Computer Applications, 142(6), 8-13.
Barnouti, N. H. (2016). Improve Face Recognition Rate Using Different Image Pre-Processing Techniques. American Journal of Engineering Research, 5(4), 46-53.
Barnouti, N. H., Mahmood, S. S., & Matti, W. E. (2016, September). Face Recognition: A Literature Review. International Journal of Applied Information Systems, 11, 21-31.
Barnouti, N. H., Matti, W. E., Al-Dabbagh, S. S., & Naser, M. A. (2016). Face Detection and Recognition Using Viola-Jones with PCA-LDA and Square Euclidean Distance. International Journal of Advanced Computer Science and Applications, Vol. 7, No. 5, 7(5), 371-377.
Bheleet, S. G., & Mankar, V. H. (2012, October). A Review Paper on Face Recognition Techniques. International Journal of Advanced Research in Computer Engineering & Technology, 1(8), 339-346.
Bolme, D. S., & Strout, M. (2007). FacePerf: Benchmarks for Face Recognition Algorithms. 10th International Symposium on Workload Characterization (pp. 2-7). Boston, MA, USA: IEEE.
Dhriti, & Kaur, M. (2012, December). K-Nearest Neighbor Classification Approach for Face and Fingerprint at Feature Level Fusion. International Journal of Computer Applications, 60(14), 13-17.
Ding, C., & Tao, D. (2016, June). Pose-invariant face recognition with homography-based normalization. Pattern Recognition-Elsevier, 1-9.
Gawande, M. P., & Agrawal, D. G. (2014, February). Face recognition using PCA and different distance classifiers. Journal of Electronics and Communication Engineering, 9(1), 01-05.
Gumede, A., Viriri, S., & Gwetu, M. (2017). Hybrid Component-based Face Recognition. Conference on Information Communication Technology and Society (pp. 1-6). Umhlanga, South Africa: IEEE.
Hu, C., Ye, M., Ji, S., Zeng, W., & Lu, X. (2015, July 21). A new face recognition method based on image decomposition for single sample per person problem. Neurocomputing-Elsevier , 160, 287-299.
Hu, G., Chan, C. H., Yan, F., Christmas, W., & Kittler, J. (2014, October). Robust face recognition by an albedo based 3D morphable. International Joint Conference on Biometrics (pp. 1-8). Clearwater, FL, USA: IEEE.
Hyv¨arinen, A., Karhunen, J., & Oja, E. (2011). Independent Component Analysis. New York: JOHN WILEY & SONS, INC.
Ibrahim, R., & Zin, Z. M. (2011, September 08). Study of Automated Face Recognition System for Office Door Access Control Application. 3rd International Conference on Communication Software and Networks (pp. 132-136). Xi’an, China: IEEE.
Jafri, R., & Arabnia, H. R. (2009, June). A Survey of Face Recognition Techniques. Journal of Information Processing Systems, 5(2), 41-68.
Jain, A. K. (2016). Human Facial Expression Recognition from Static Images using Shape and Appearance Feature. 2nd International Conference on Applied and Theoretical Computing and Communication Technology (pp. 598-603). Bangalore, India: IEEE.
Kamerikar, U. A., & Chavan, M. (2014, February ). Experimental Assessment of LDA and KLDA for Face Recognition. International Journal of Advance Research in Computer Science and Management Studies, 2(2), 137-146.
Karande, K. J., & Badage, R. N. (2016). Facial Feature Extraction using Independent Component Analysis. Annual Int’l Conference on Intelligent Computing, Computer Science & Information Systems (pp. 1-4). Pattaya (Thailand): IAE.
Kaur, N. (2016, May – Jun). Review of Face Recognition System Using MATLAB. International Journal of Computer Science Trends and Technology, 4(3), 30-33.
Kurmi, U. S., Agrawal, D., & Baghel, R. K. (2014, February 01). Study of Different Face Recognition Algorithms and Challenges. International Journal of Engineering Research, 3(2), 112-115.
Li, S. Z., & Jain, A. K. (2011). Handbook of Face Recognition (Second ed.). London : Springer-Verlag London Limited.
Liu, Z., Lv, L., & Yong, W. (2016). Development of face Recognition System Based on peA and LBP for Intelligent Anti-Theft Doors. International Conference on Computer and Communications (pp. 341-346). Chengdu, China: IEEE.
Murtaza, M., Sharif, M., Raza, M., & Shah, J. H. (2014, March). Face Recognition Using Adaptive Margin Fisher’s Criterion and Linear Discriminant Analysis (AMFC-LDA). The International Arab Journal of Information Technology, 11(2), 149-158.
Parmar, D. N., & Mehta, B. B. (2013, January). Face Recognition Methods & Applications. Computer Technology & Applications, 4(1), 84-86.
Parveeni, P., & Thuraisingham, B. (2006). Face Recognition using Multiple Classifiers. 18th International Conference on Tools with Artificial Intelligence (pp. 1-8). Arlington, VA, USA: IEEE.
Patel, A., & Smith, W. A. (2009). 3D Morphable Face Models Revisited. Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1327-1334). Miami, FL, USA: IEEE.
Phaneemdra, P., Ramachandran, V., & Reddy, E. S. (2015). Human Face Detection and Recognition using PCA and DCT in HMM. International Journal of Scientific Engineering and Technology Research, 4(35), 7080-7085.
Poon, B., Amin, M. A., & Yan, H. (2016, August 27). PCA Based Human Face Recognition with Improved Methods for Distorted Images due to Illumination and Color Background. International Journal of Computer Science, 1-7.
Sandhu, P. S., Kaur, I., Verma, A., Kaur, I., & Kumari, S. (2009). Face Recognition Using Eigen face Coefficients and Principal Component Analysis. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 3(4), 1039-1043.
Setyadi, A. D., & Tri Harsono, S. W. (2015). Human Character Recognition Application Based on Facial Feature Using Face Detection. International Electronics Symposium (pp. 263-267). Surabaya, Indonesia: IEEE.
Shah, D. H., Shah, J. S., & Shah, T. V. (2014, February). The Exploration of Face Recognition Techniques. International Journal of Application or Innovation in Engineering & Management, 3(2), 238-246.
Sharma, N., & Dubey, S. K. (2014, April). Face Recognition Analysis Using PCA, ICA and Neural Network. International Journal of Digital Application & Contemporary Research, 2(9), 1-8.
Shekhar, A., Murthy, V., & Natarajan, S. (2015). Face Recognition using Gabor Wavelet Features with PCA and KPCA – A Comparative Study. 3rd International Conference on Recent Trends in Computing. 57, pp. 650 – 659. Karanataka, India: Elsevier.
Slavković, M., & Jevtić, D. (2012, February). Face Recognition Using Eigenface Approach. Serbian journal of electrical engineering, 9(1), 121-130.
Sodhi, K. S. (2013, July). Comparative Analysis of PCA-based Face Recognition System using different Distance Classifiers. International Journal of Application or Innovation in Engineering & Management, 2(7), 341-348.
Sodhi, K. S., & Lal, M. (2013, March). Face recognition using pca, lda and various distance classifiers. Journal of Global Research in Computer Science, 4(3), 30-35.
Soula, A., Said, S. B., Ksantini, R., & Lachiri, Z. (2016). A Novel Kemelized Face Recognition System. 4th International Conference on Control Engineering & Information Technology (pp. 1-5). Tunisia, Hammamet: IEEE.
Sun, Y., Chen, X., & Yin, M. R. (2010, May). Tracking Vertex Flow and Model Adaptation for Three-Dimensional Spatiotemporal Face Analysis. IEEE RFID Virtual Journal, 40(3), 461-474.
Swets, D., & Weng, J. (1996, Auggest). Using Discriminant Eigenfeatures for Image Retrieval. Transaction on pattern analysis and machine intelligence, 18(8), 831 – 836.
Toygar, Ö., & Acan, A. (2003). Face Recognition using PCA, LDA and ICA approaches on colored images. Journal of Electrical & Electronics Engineering, 3(1), 735-743.
Wang, Y., & Zhang, Y. (2010). Facial Recognition Based on Kernel PCA. Third International Conference on Intelligent Networks and Intelligent Systems (pp. 88-91). Shenyang, China: IEEE.
Wiskott, L., Fellous, J.-M., Kruger, N., & Malsburg, C. v. (1997, July ). Face Recognition by Elastic Bunch Graph Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 775-779.
Yu, J., & Li, C. (2013). Face Recognition Based on Euclidean Distance and Texture Features. International Conference on Computational and Information Sciences (pp. 211-213). Shiyang, China: IEEE.
Zafaruddin, G. M., & Fadewar, D. H. (2014). Face Recognition: A Holistic Approach Review. International Conference on Contemporary Computing and Informatics (pp. 175-178). Mysore, India: IEEE.
Zbeda, F. G., Abdulaziz, M. H., & Saleh, A. E. (2016, September). PCA-HOG Descriptors for Face Recognition in very Small Images. International Journal of Advanced Research in Computer Science and Software Engineering, 6(9), 449-451.