Abstract
The process of Face Recognition is complicated due to the background, pose variations in the images. Using the Pre-processing techniques proposed in this paper the essential invariant features in an image have been made available for extraction. Background Removal based on Eccentricity is implemented by incorporating both YCbCr and HSV color models to eliminate unnecessary features in the background. Multi-scaled fusion is included for nullifying the variation in pose. Next, the images are subjected to feature extraction using two-dimensional Discrete Wavelet Transform (DWT) and feature selection algorithm. Experimental results show the effectiveness of the above mentioned techniques for face recognition on two benchmark face databases, namely, CMU-PIE and Caltech.
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Lawrence, A., Manoj Ashwin , N.V., Manikantan, K. (2017). Face Recognition Using Background Removal Based on Eccentricity and Area Using YCbCr and HSV Color Models. In: Lobiyal, D., Mohapatra, D., Nagar, A., Sahoo, M. (eds) Proceedings of the International Conference on Signal, Networks, Computing, and Systems. Lecture Notes in Electrical Engineering, vol 395. Springer, New Delhi. https://doi.org/10.1007/978-81-322-3592-7_4
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DOI: https://doi.org/10.1007/978-81-322-3592-7_4
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