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This paper investigates how far a very deep neural network is from attaining close to saturating performance on existing 2D and 3D face alignment datasets. To this end, we make the following 5 contributions: (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large...
Tolerance (“invariance”) to identity-preserving image variation (e.g. variation in position, scale, pose, illumination) is a fundamental problem that any visual object recognition system, biological or engineered, must solve. While standard natural image database benchmarks are useful for guiding progress in computer vision, they can fail to probe the ability of a recognition system to solve the invariance...
Over the past few years, multi-view face detection issue has become one of the most attractive research topics in the field of computer vision. In this paper, a novel automatic system for multi-view face detection and pose estimation is proposed. Our approach adopts modified appearance-based learning methods to build corresponding face detectors and pose estimators, and detects multi-view faces according...
Performance of face recognition system has not been satisfied due to the change of illumination on facial image. Thus, there were many proposals that dealing with illumination compensation in face recognition in the past decades. In this paper, we propose a wavelet based illumination invariant algorithm based on the illumination-reflectance model. The proposed method aims to remove illumination component...
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