Abstract
Pose, illumination, expression and other transitive and demographic variates present in the facial images have significant effects on the performance of face recognition system. A Gibbs sampler based statistical simulation algorithm is presented to evaluate the performance of EBGM based face recognition system. A new set of microscopic and stochastic image features are proposed which takes key role in determining the quality of facial images. Effects of these features on the performance of the EBGM based face recognition system are evaluated using an algorithm based on random effects model and Gibbs sampler.
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Chakraborty, M., Chanda, K., Mazumdar, D. (2012). A Novel Statistical Model to Evaluate the Performance of EBGM Based Face Recognition. In: Kundu, M.K., Mitra, S., Mazumdar, D., Pal, S.K. (eds) Perception and Machine Intelligence. PerMIn 2012. Lecture Notes in Computer Science, vol 7143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27387-2_36
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DOI: https://doi.org/10.1007/978-3-642-27387-2_36
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