Abstract
Prediction of the best ICA architecture for face recognition systems is somewhat complicated. This paper shows how the recognition performance of both architectures depends on the nature of feature vectors rather than several criteria such as different databases, number of subjects, and number of principle components. The investigation finds that Architecture-II yields the better performance than Architecture-I based on face feature vectors. The experiments are done on different face datasets like FERET, ORL, CVL, and YALE.
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Acknowledgments
The work presented here is being conducted in the Biometrics Laboratory and Bio-Medical Infrared Image Processing Laboratory of Department of Computer Science and Engineering of Tripura University (A Central University), Tripura, Suryamaninagar-799022. The research work was supported by the Grant No. 12(2)/2011-ESD, Dated 29/03/2011 from the DeitY, MCIT, Government of India and also supported by the Grant No. BT/533/NE/-TBP/2013, Dated 03/03/2014 from the Department of Biotechnology (DBT), Government of India. The authors would like to thank Prof. Barin Kumar De, Department of Physics, Tripura University (A Central University) and Dr. Debotosh Bhattacharjee, Associate Professor, Department of Computer Science and Engineering, Jadavpur University for their kind support to carry out this research work.
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Singha, A., Bhowmik, M.K., Dhar, P., Ghosh, A.K. (2015). Analysis and Performance Evaluation of ICA-Based Architectures for Face Recognition. In: Barneva, R., Bhattacharya, B., Brimkov, V. (eds) Combinatorial Image Analysis. IWCIA 2015. Lecture Notes in Computer Science(), vol 9448. Springer, Cham. https://doi.org/10.1007/978-3-319-26145-4_24
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DOI: https://doi.org/10.1007/978-3-319-26145-4_24
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