Face Recognition under Real-world Conditions
Ladislav Lenc, Pavel Král · 2013
This paper deals with Automatic Face Recognition (AFR). The main contribution of this work consists in the evaluation of our two previously proposed AFR methods in real conditions. At first, we compare and evaluate the recognition accuracy of two AFR methods on well-controlled face database. Then we compare these results with the recognition accuracy on a real-world database of comparable size. For such comparison, we use a sub-set of the newly created Czech News Agency (CTK) database. This database is created from the real photos acquired by the CTK and the creation of this corpus represents the second contribution of this work. The experiments show the significant differences in the results on the controlled and real-world data. 100% accuracy is achieved on the ORL database while only 72.7% is the best score for the CTK database. Further experiments show, how the recognition rate is influenced by the number of training images for each person and by the size of the database. We also demonstrate, that the recognition rate decreases significantly with larger database. We propose a confidence measure technique as a solution to identify and to filter-out the incorrectly recognized faces. We further show that confidence measure is very beneficial for AFR under real