Search

Abstract: Due to the rapid growth of social network services such as Facebook and Twitter, incorporation of face recognition in these large-scale web services is attracting much attention in both academia and industry. The major problem in such applications is to deal efficiently with the growing number of samples as well as local appearance variations caused by diverse environments for the millions of users over time. In this paper, we focus on developing an incremental face recognition method for Twitter application. Particularly, a data-independent feature extraction method is proposed via binarization of a Gabor filter. Subsequently, the dimension of our Gabor representation is reduced considering various orientations at different grid positions. Finally, an incremental neural network is applied to learn the reduced Gabor features. We apply our method to a novel application which notifies new photograph uploading to related users without having their ID being identified. Our extensive experiments show that the proposed algorithm significantly outperforms several incremental face recognition methods with a dramatic reduction in computational speed. This shows the suitability of the proposed method for a large-scale web service with millions of users. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a performance driven methodology for cancelable face templates generation. This is to address the issue of satisfying both the security and performance requirements at the same time. Essentially, the methodology consists of two transformations namely, an efficient feature extraction transformation and an error minimizing template transformation. The first transformation is achieved via a modified sparse random projection which extracts and transforms essential face features into cancelable templates. The second transformation is realized through a direct objective formulation to minimize the system''s total error rate. In order to facilitate convergence of the resulted minimization search, a modified sigmoid is proposed for an error counting step function approximation. Using two publicly available face databases, we empirically show an improved verification performance in terms of the equal error rate while hiding the face identity simultaneously. [Copyright &y& Elsevier]