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Facial landmark detection has proved to be a very challenging task in biometrics due to the numerous sources of variation. In this work, we present an algorithm for robust detection of facial component-landmarks. Specifically, we address the variation due to extreme pose and illumination. To achieve robust detection for extreme poses, we use a set of independent pose and landmark specific detectors...
Landmark detection has proven to be a very challenging task in biometrics. In this paper, we address the task of facial component-landmark detection. By “component” we refer to a rectangular subregion of the face, containing an anatomical component (e.g., “eye”). We present a fully-automated system for facial component-landmark detection based on multi-resolution isotropic analysis and adaptive bag-of-words...
The silhouette of the face profile is a well-known biometric that is already in use in face recognition research. One of the challenges for successful employment of this biometric is the sensitivity of its geometry to face rotation. In this paper, we introduce a new method that improves robustness to rotation. We achieve this by exploring the feature space of profiles under various rotations with...
In this paper, we present a fully automatic system for face recognition based on a silhouette of the face profile. Previous research has demonstrated the high discriminative potential of this biometric. However, for the successful employment of this characteristic one is confronted with many challenges, such as the sensitivity of a profile's geometry to face rotation and the difficulty of accurate...
In this paper, we present Local Feature Hashing (LFH), a novel approach for face recognition. Focusing on the scalability of face recognition systems, we build our LFH algorithm on the p-stable distribution Locality-Sensitive Hashing (pLSH) scheme that projects a set of local features representing a query image to an ID histogram where the maximum bin is regarded as the recognized ID. Our extensive...
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