Abstract—It is known that the use of video can help improve the performance of face verification systems. However, processing video in resource constrained devices is prohibitive. In order to reduce the load of the algorithms, a quality-based selection of frames can be applied. Generally there are available several qualities and thus a good fusion scheme is required. This paper addresses the problem of fusing quality measures such that the resulting quality improves the performance of frame selection. A comparison of different methods for fusing qualities is presented. Also, some new quality measures based on time derivatives are proposed, which are shown to be beneficial for estimating the overall quality. Finally, a curve is proposed which proves that the qualities used for frame selection effectively improve verification performance, independent of the number of frames selected or the method employed for obtaining the overall biometric score.

Abstract This paper presents an incremental learning solution for Linear Discriminant Analysis (LDA) and its applications to object recognition problems. We apply the sufficient spanning set approximation in three steps i.e. update for the total scatter matrix, between-class scatter matrix and the projected data matrix, which leads an online solution which closely agrees with the batch solution in accuracy while significantly reducing the computational complexity. The algorithm yields an efficient solution to incremental LDA even when the number of classes as well as the set size is large. The incremental LDA method has been also shown useful for semi-supervised online learning. Label propagation is done by integrating the incremental LDA into an EM framework. The method has been demonstrated in the task of merging large datasets which were collected during MPEG standardization for face image retrieval, face authentication using the BANCA dataset, and object categorisation using the Caltech101 dataset.

Abstract—Person recognition using facial features, e.g., mug-shot images, has long been used in identity documents. However, due to the widespread use of web-cams and mobile devices embedded with a camera, it is now possible to realize facial video recognition, rather than resorting to just still images. In fact, facial video recognition offers many advantages over still image recognition; these include the potential of boosting the system accuracy and deterring spoof attacks. This paper presents an evaluation of person identity verification using facial video data, organized in conjunction with the International Conference on Biometrics (ICB 2009). It involves 18 systems submitted by seven academic institutes. These systems provide for a diverse set of assumptions, including feature representation and preprocessing variations, allowing us to assess the effect of adverse conditions, usage of quality information, query selection, and template construction for video-to-video face authentication. Index Terms—Biometric authentication, face video recognition. I.