Comparison of Face Recognition Algorithms for Human-Robot Interactions
DOI:
https://doi.org/10.11113/jt.v72.3887Keywords:
Algorithm comparison, eigen faces, face recognition, fisher faces, LBPH, OpenCVAbstract
Face recognition is a cornerstone of many robotic systems in which a robot has to identify and interact with a human being. Choosing a face recognition algorithm arbitrarily may not yield the best results for a researcher and may produce undermined results. In this paper we compare three widely used algorithms in terms of speed and accuracy. Such data can be very useful in choosing an algorithm for a particular task. The algorithms were applied to 36 different situations, and the results indicate the strengths, advantage and limitations of each of the three recognition methods in a certain setting.
References
Mankame D. P. and Nayeem, S. 2013. Face Recognition using PCA and LDA: Analysis and Comparison. In Communication and Computing (ARTCom 2013), Fifth International Conference on Advances in Recent Technologies. 1–6.
Dubey M. and Jain, P. 2013. Comparison of PCA and LDA based Face Recognition Technique in Noisy Environment. Int. J. Res. Comput. Appl. Manag. 3(8): 9–13
Sajjan S. 2013. Comparison of PCA and LDA for Face Recognition. Int. J. Eng. Res. Technol. 2(7): 1901–1904.
Mankar V. and Bhele, S. 2012. A Review Paper on Face Recognition Techniques. International Journal of Advanced Research in Computer Engineering & Technology. 1(8): 339–346.
Ramkumar G. and Manikandan, M. 2013. Face Recognition-Survey. Sciencepublication.org. 260–268.
Ahonen T., Hadid, A. and Pietik. 2004. Face Recognition with Local Binary Patterns. Computer Vision Proceedings. 469–481.
Bradski G. 2000. The OpenCV Library. Dr. Dobb’s J. Softw. Tools.
Bradski G. and Kaehler. 2008. Learning OpenCV. O’Reilly Media, Inc.
Lee K., Ho, J. and Kriegman. 2005. Acquiring Linear Subspaces for Face Recognition Under Variable Lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence. 27(5): 684–698.
Georghiades A, S. and Belhumeur, P. N. 2001. From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose. IEEE Transactions on Pattern Analysis and Machine Intelligence. 23(6): 643–660.
Gorodnichy D. 2009. Face Databases and Evaluation. Encycl. Biometrics. 1–9.
George J. 2012. Performance Comparison of Face Recognition using Transform Domain Techniques. World of Computer Science and Information Technology Journal (WCSIT). 2(3): 82–89.
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