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Face Recognition using Radial Curves and Back Propagation Neural Network for Frontal Faces under Various Challenges

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IJCA Proceedings on International Conference on Advances in Science and Technology
© 2016 by IJCA Journal
ICAST 2015 - Number 3
Year of Publication: 2016
Authors:
Latasha Keshwani
D. J. Pete

Latasha Keshwani and D j Pete. Article: Face Recognition using Radial Curves and Back Propagation Neural Network for Frontal Faces under Various Challenges. IJCA Proceedings on International Conference on Advances in Science and Technology ICAST 2015(3):11-15, February 2016. Full text available. BibTeX

@article{key:article,
	author = {Latasha Keshwani and D.j. Pete},
	title = {Article: Face Recognition using Radial Curves and Back Propagation Neural Network for Frontal Faces under Various Challenges},
	journal = {IJCA Proceedings on International Conference on Advances in Science and Technology},
	year = {2016},
	volume = {ICAST 2015},
	number = {3},
	pages = {11-15},
	month = {February},
	note = {Full text available}
}

Abstract

A unique framework is proposed, in which the analysis of 3D faces is carried out on a readily available ORL database. The work is executed on different steps of preprocessing, feature extraction, face restoration, face classification and face recognition. In this novel framework, radial curves are applied for representing the facial surface. This representation shows robustness to various challenges such as occlusions (i. e. wearing glasses, growth of hair), different poses, expressions, and missing parts due to illumination. The face is represented by radial curves on it, starting from nose to the end of the face which helps in further comparison of the face with their corresponding curves. Further Neural Network is employed in this system. The performance analysis is carried out for radial curve based system and neural network based system

References

  • H. Drira, B. Ben Amor, A. Srivastava, M. Daoudi, and R. Slama "3D Face Recognition under Expressions, Occlusions, and Pose Variations," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 9, pp. 2270-2283, 2013.
  • I. A. I. Kakadiaris, G. Passalis, G. Toderici, M. N. M. Murtuza, Y. Lu, N. Karampatziakis, and T. Theoharis, "Three-Dimensional Face Recognition in the Presence of Facial Expressions: An Annotated Deformable Model Approach," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 640-649, 2007.
  • Y. Lee, H. Song, U. Yang, H. Shin, and K. Sohn, "Local Feature Based 3D Face Recognition," Proc. Audio- and Video-Based Biometric Person Authentication, pp. 909-918, 2005.
  • C. C. C. Queirolo, L. Silva, O. R. O. Bellon, and M. P. M. Segundo,"3D Face Recognition Using Simulated Annealing and the Surface Interpenetration Measure," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 2, pp. 206-219, 2010.
  • G. Passalis, P. Perakis, T. Theoharis, and I. A. I. Kakadiaris, "Using Facial Symmetry to Handle Pose Variations in Real-World 3D Face Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no. 10, pp. 1938-1951, 2011.
  • S. Elaiwat, M. Bennamoun, F. Boussaid, and A. El-Sallam, "3-D Face Recognition Using Curvelet Local Features," IEEE Signal Processing Letters, vol. 21, no. 2, pp. 172-175, 2014.
  • H. Mohammadzade and D. Hatzinakos, "Iterative Closest Normal Point for 3D Face Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 2, pp. 381-397, 2013.
  • D. Smeets, P. Claes, J. Hermans, D. Vandermeulen, and P. Suetens, "A Comparative Study of 3-D Face Recognition Under Expression Variations," IEEE Transactions on systems, man, and cybernetics—part c: applications and reviews, vol. 42, no. 5, pp. 710-727, 2012.
  • N. Alyuz, B. Gokberk, and L. Akarun, "3-D Face Recognition Under Occlusion Using Masked Projection," IEEE Transactions on Information Forensics and Security, vol. 8, no. 5, pp. 789-802, 2013.
  • N. Erdogmus and J-L. Dugelay, "3D Assisted Face Recognition: Dealing With Expression Variations," IEEE Transactions on Information Forensics and Security, vol. 9, no. 5, pp. 826-838, 2014.
  • A. M. Ali, "A 3D-Based Pose Invariant Face Recognition at a Distance Framework," IEEE Transactions on Information Forensics and Security, vol. 9, no. 12, pp. 2158-2169, 2014.
  • G. Goswami, M. Vasta, and R. Singh, "RGB-D Face Recognition with Texture and Attribute Features," IEEE Transactions on Information Forensics and Security, vol. 9. , no. 10, pp. 1629-1640, 2014.
  • R. Min, N. Kose, and J. Dugelay, "KinectFaceDB: A Kinect Database for Face Recognition," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 44, no. 11, pp. 1534-1548, 2014.
  • U. prabhu, J. Heo, and M. Savvides, "Unconstrained Pose-invariant Face Recognition Using 3D Generic Elastic Models," IEEE Transactions on pattern Analysis and Machine Intelligence, vol. 33, no. 10, pp. 1952-1961, 2011.
  • C. H. Li and I. Gondra, "A Novel Neural Network-Based Approach for Multiple Instance Learning," IEEE International Conference on Computer and Information Technology (CIT 2010), pp. 451-456, 2010.
  • H. Zhou, A. Mian, L. Wei, D. Creighton, M. Hossny, and S. Nahavandi, "Recent Advances on Singlemodal and Multimodal Face Recognition: A Survey," IEEE Transactions On Human-Machine Systems, vol. 44, no. 6, pp. 701-716, 2014.
  • Y. Woo, C. Yi, and Y. Yi, "FAST PCA-BASED FACE RECOGNITION ON GPUS," IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP 2013), pp. 2659-2663, 2013.
  • L. -H. Chan, S. -H. Salleh, C. -M. Ting, and A. K. Ariff, "PCA and LDA-Based Face Verification Using Back-Propagation Neural Network," IEEE 10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010), pp. 728-732, 2010.
  • J. Yang, H. Li, and Y. Jia, "Go-ICP:Solving 3D Registration Ef?ciently and Globally Optimally ," IEEE International Conference on Computer - Vision, pp. 1457-1464, 2013.