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We present a new approach to robust pose-variant face recognition, which exhibits excellent generalization ability even across completely different datasets due to its weak dependence on data. Most face recognition algorithms assume that the face images are very well-aligned. This assumption is often violated in real-life face recognition tasks, in which face detection and rectification have to be...
Feature misalignment in object detection refers to the phenomenon that features which fire up in some positive detection windows do not fire up in other positive detection windows. Most often it is caused by pose variation and local part deformation. Previous work either totally ignores this issue, or naively performs a local exhaustive search to better position each feature. We propose a learning...
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