Abstract
Computational and storage space efficiencies of a novel approach based on appearance-based statistical methods for face recognition are studied. The new approach is a low-complexity divide-and-conquer method implemented as a multiple-classifier system. Appearance-based statistical algorithms are used for dimensionality reduction followed by distance-based classifiers. An appropriate classifier combination method is used to determine the resulting face recognized. FERET database and FERET Evaluation Methodology are used in all experimental evalua- tions. Time and space complexities of the proposed approach indicate that it outperforms the holistic Principal Component Analysis, Linear Discriminant Analysis and Independent Component Analysis in computational and storage space efficiencies. The experimental results show that the proposed approach also provides better recognition performance on frontal images.
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References
Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(2), 228–233 (2001)
Toygar, Ö., Acan, A.: An Analysis of Appearance-Based Statistical Methods and Autoassociative Neural Networks on Face Recognition. In: The 2003 International Conference on Artificial Intelligence (IC-AI 2003), Las Vegas, Nevada, USA (2003)
Hyvärinen, A.: Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Transactions on Neural Networks 10(3), 626–634 (1999)
Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face recognition: A literature survey. Technical Report CAR-TR-948, CS-TR-4167, N00014-95-1-0521 (2000)
Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.: The FERET Database and Evaluation Procedure for Face Recognition Algorithms. Image and Vision Computing Journal 16(5), 295–306 (1998)
Phillips, P.J., Rauss, P.J., Der, S.Z.: FERET (Face Recognition Technology) Recognition Algorithm Development and Test Results. Technical Report, AR-TR-995, Army Research Laboratory (1996)
Achermann, B., Bunke, H.: Combination of Face Recognition Classifiers for Person Identification. In: Proceedings of 13th IAPR International Conference in Pattern Recognition (ICPR 1996), Vienna, Austria, pp. 416–420 (1996)
Ho, T., Hull, J., Srihari, S.: Decision Combination in Multiple Classifier Systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(1), 66–75 (1994)
Cormen, T.H., Leiserson, C.E., Rivest, R.L.: Introduction to Algorithms. TheElectrical Engineering and Computer Science Series (1990)
Toygar, Ö., Acan, A.: Multiple Classifier Implementation of a Divide-and-Conquer Approach Using Appearance-Based Statistical Methods for Face Recognition. Pattern Recognition Letters (2004) (accepted for publication)
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Toygar, Ö., Acan, A. (2004). Boosting Face Recognition Speed with a Novel Divide-and-Conquer Approach. In: Aykanat, C., Dayar, T., Körpeoğlu, İ. (eds) Computer and Information Sciences - ISCIS 2004. ISCIS 2004. Lecture Notes in Computer Science, vol 3280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30182-0_44
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DOI: https://doi.org/10.1007/978-3-540-30182-0_44
Publisher Name: Springer, Berlin, Heidelberg
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