Face Recognition Using Location Averaging and Intensity’s Position Estimation Techniques for Human Authentication
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2018-10-02 https://doi.org/10.14419/ijet.v7i4.10.20697 -
Biometrics, Face recognition, Human recognition system, Gray averaging, Location averaging, Intensity’s position estimation -
Abstract
Face recognition is an effective tool in the biometric human recognition system. In this competitive world, several techniques and systems are emerging to satisfy the needs of the face recognition system’s performance. To obtain the high-performance ratio novel techniques are combined and created a new face recognition system. Spatial domain techniques like Gray averaging technique, Location averaging technique and Intensity’s position estimation technique are united with frequency domain technique like Discrete Cosine Transform. Intensity’s position estimation is a novel feature extraction and classification technique proposed in this work. Three standard face databases are tested using this system. Accuracy and runtime are major parameters used to validate the obtained results. The maximum accuracy rate of about 86% is obtained.  Â
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References
[1] Weilong Chen, Meng Joo Er, and Shiqian Wu, Illumination Compensation and Normalization for Robust Face Recognition Using Discrete Cosine Transform in Logarithm Domain, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, Vol. 36, No. 2, April 2006, Pp 458-466.
[2] Somaye Ahmadkhani, Peyman Adibi, Face recognition using supervised probabilistic principal component analysis mixture model in dimensionality reduction without loss framework, IET Computer Vision, 2016, Vol. 10, Issue 3, pp. 193–201.
[3] Vinay. A, Vinay S. Shekhar, Akshay Kumar. C, Natarajan. S, K.N. Balasubramanya Murthy, Affine-scale invariant feature transform and two-dimensional principal component analysis: a novel framework for affine and scale invariant face recognition, IET Computer Vision, 2016, Vol. 10, Issue 1, pp. 43–59.
[4] Zahid Mahmood, Tauseef Ali, Samee U. Khan, Effects of pose and image resolution on automatic face recognition, IET Biometrics, 2016, Vol. 5, Issue 2, pp. 111–119.
[5] Parivazhagan A and BrinthaTherese A. (2018) Combined Analysis of Image Processing Transforms with Location Averaging Technique for Facial and Ear Recognition System, Computational Signal Processing and Analysis, Lecture Notes in Electrical Engineering, Volume 490, Springer, Singapore, pp 67-77.
[6] Parivazhagan A., BrinthaTherese A, A Novel 2D Face, Ear Recognition System Using Max–Min Comparison Technique for Human Identification, Artificial Intelligence and Evolutionary Computations in Engineering Systems, Advances in Intelligent Systems and Computing (AISC), Vol 517, Springer, Singapore, July 2017, pp 695-704.
[7] Chunlei Peng, Xinbo Gao, Nannan Wang, and Jie Li, Graphical Representation for Heterogeneous Face Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 2, February 2017, pp. 301-312.
[8] June-Young Jung, Seung-Wook Kim, Cheol-Hwan Yoo, Won-Jae Park, and Sung-Jea Ko, LBP-Ferns-Based Feature Extraction for Robust Facial Recognition, IEEE Transactions on Consumer Electronics, Vol. 62, No. 4, November 2016, pp. 446-453.
[9] Srinivasa Perumal Ramalingam, P.V.S.S.R. Chandra Mouli, Two-level dimensionality reduced local directional pattern for face recognition, International Journal Biometrics, Vol. 8, No. 1, 2016, pp.52-64.
[10] Libor Spacek's Facial Images Databases: http://cmp.felk.cvut.cz/~spacelib/faces/ (URL).
[11] Georgia Tech Face Database, http://www.anefian.com/research/face_reco.htm (URL)
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How to Cite
A, P., & Brintha Therese.A, D. (2018). Face Recognition Using Location Averaging and Intensity’s Position Estimation Techniques for Human Authentication. International Journal of Engineering & Technology, 7(4.10), 24-27. https://doi.org/10.14419/ijet.v7i4.10.20697Received date: 2018-10-01
Accepted date: 2018-10-01
Published date: 2018-10-02