Archana Sapkota

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To date, almost all experimental evaluations of machine learning-based recognition algorithms in computer vision have taken the form of "closed set" recognition, whereby all testing classes are known at training time. A more realistic scenario for vision applications is "open set" recognition, where incomplete knowledge of the world is present at training(More)
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Scale is one of the major challenges in recognition problems. For example, a face captured across large distances is considerably harder to recognize than the same face at small distances. Local binary pattern (LBP) and its variants have been successfully used in face detection, recognition, and many other computer vision applications. While LBP features(More)
While modern research in face recognition has focused on new feature representations, alternate learning methods for fusion of features, most have ignored the issue of unmodeled correlations in face data when combining diverse features such as similar visual regions, attributes, appearance frequency , etc. Conventional wisdom is that by using sufficient(More)
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