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Confidence Measure for Automatic Face Recognition

This paper deals with the use of confidence measure for Automatic Face Recognition (AFR). AFR is realized by the adapted Kepenecki face recognition approach based on the Gabor wavelet transform. This work is motivated by the fact that obtained recognition rate on the real-world corpus is only about 50% which is not sufficient for our application, a system for automatic labelling of the photographs in a large database. The main goal of this work is thus the proposition of the post-processing of the classification result in order to remove the “incorrectly” classified face images. We show that the use of confidence measure to filter out incorrectly recognized faces is beneficial. Two confidence measures are proposed and evaluated on the Czech News Agency (CTK) corpus. Experimental results confirm the benefit of the use of confidence measure for the automatic face recognition task.