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In video based face recognition, face images are typically captured over multiple frames in uncontrolled conditions, where head pose, illumination, shadowing, motion blur and focus change over the sequence. Additionally, inaccuracies in face localisation can also introduce scale and alignment variations. Using all face images, including images of poor quality, can actually degrade face recognition...
We propose a novel face image similarity measure based on Hausdorff distance (HD). In contrast to conventional HD-based measures, which are generally applied in the image space (such as edge maps or gradient images), the proposed HD-based similarity measure is applied in the feature space. By extending the concept of HD using a variable radius and reference set, we can generate a neighbourhood set...
Under the constraint of using only a single gallery image per person, this paper proposes a fast multi-class pattern classification approach to 2D face recognition robust to changes in pose, illumination, and expression (PIE). This work has three main contributions: (1) we propose a representative face space method to extract robust features, (2) we apply a learning method to weight features in pairs,...
Although automatic identity inference based on faces has shown success when using high quality images, for CCTV based images it is hard to attain similar levels of performance. Furthermore, compared to recognition based on static images, relatively few studies have been done for video based face recognition. In this paper, we present an empirical analysis and comparison of face recognition using high...
Most face recognition systems only work well under quite constrained environments. In particular, the illumination conditions, facial expressions and head pose must be tightly controlled for good recognition performance. In 2004, we proposed a new face recognition algorithm, adaptive principal component analysis (APCA) (Blanz and Vetter, 1999), which performs well against both lighting variation and...
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