700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ ReadersThis Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)
Research Article Open Access
In many real-time face recognition systems such as e-passport, law enforcement and ID card identification, there is usually only a single sample per person (SSPP) enrolled in these systems, and many existing face recognition methods may fail to work well because there are not enough samples for discriminative feature extraction in this scenario. However, the probe samples of these face recognition systems are usually captured on the spot, and it is possible to collect multiple face images per person for on-location probing, which is potentially useful to improve the recognition performance. In this paper, we propose a method based on locality repulsion projections (LRP) and Histograms to address the problem of SSPP face recognition using Multiple Sample per Person (MSPP). The LRP method is motivated by our observation that similar face images from different people may lie in a locality in the feature space and cause misclassifications. We propose the method with the aim of separating the samples of different classes within a neighbor hood through GMM for easier classification. To better characterize the similarity between each gallery face and the probe image set, we propose a GMM method for assigning a label to each probe image set. Finally, we measure the similarity between the images using Euclidean distance formulae.