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Person authentication using nearest feature line embedding transformation and biased discriminant analysis

Personal authentication (PA) on smartphones plays the crucial role in mobile payment. Facial features are the most user-friendly biometric feature because of the build-in camera, when we use smartphones as the payment devices. In this study, a novel authenticated method is proposed for PA by integrating feature line embedding (FLE) transformation and biased discriminant analysis (BDA) by using facial features. Due to the few training samples, the discriminant power is limited for learning. In feature spaces, feature lines are regarded as the feature combination between two training samples and infinitely simulate the possible features of various conditions for training. In PA, only positive samples is used to calculate the within-class scatter, and the between class scatter is also calculated using negative samples by the BDA strategy. Compared with the traditional two-class classification and BDA problems, the FLE integrates with BDA to obtain a better dimension reduction transformation. A support vector machine (SVM) classifier is further trained to determine a query sample is a real or a forged sample.