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. Author manuscript; available in PMC: 2018 Jan 27.
Published in final edited form as: IEEE Trans Image Process. 2016 Jul 27;25(10):4753–4767. doi: 10.1109/TIP.2016.2594486

Fig. 1.

Fig. 1

The main idea of Confidence Preserving Machine (CPM): (a) A standard single-margin classifier identifies true positive (TP), true negative (TN), false positive (FP) and false negative (FN). Data within the margin (dashed lines) consist of mostly FP and FN, producing undesired ambiguities for training a classifier. (b) The proposed confident classifiers, two hyperplanes that are not necessarily parallel, reveal easy and hard samples for preserving confident predictions in each class. (c) The proposed CPM, consisting of confident classifiers and a person-specific (PS) classifier using a quasi-semi-supervised (QSS) learning strategy, is trained to propagate predictions from confident test samples (easy test samples) to hard ones.