This paper proposes a new methodology for comparing two performance methods based on confidence interval for the ROC curve. The methods performed and compared are two algorithms for face recognition. The novelty of the paper is threefold: i) designing a methodology for the comparison of decision making algorithms via confidence intervals of ROC curves; ii) investigating how sample sizes influence the properties of the particular methods; iii) recommendations for a general comparison of decision making algorithms via confidence intervals of ROC curves. To support our conclusions we investigate and demonstrate several approaches for constructing parametric confidence intervals on real data. Thus, we present a non-traditional and reliable way of reporting pattern recognition results using ROC curves with confidence intervals.
This paper addresses the problem of face templates creation for facial recognition system. The application of a face recognition system in real-world conditions requires compact and representative face templates in order to maintain low error rate and low classification time. Contemporary face template creation methods are not suitable for face recognition systems with large number of users as they produce many templates per person. These templates are often redundant and their high number requires long classification time. The paper presents four approaches to face templates creation that produce one to three face templates per person. The influence of different face template creation approaches was assessed on PubFig and IFaViD database. The achieved results show that appropriate face template creation methods have a significant influence on face recognition system performance. 1.1 Related Work There are very few available publications describing the implementation of face recognition systems into real-world application. The work of Stallkamp, Ekenel, & Stiefelhagen (2007) presents overall face 724 Malach T. and Prinosil J..
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