Affine invariant image comparison is always consequential in computer vision. In this paper, affine-SURF (ASURF) is
introduced. Through a series of affine transformations and feature extraction, the matching algorithm becomes more
robust with the view and scale change. A kd-tree structure is build to store the feature sets and BBF search algorithm is
used in feature matching, then duplicates are removed by the conditional of Euclidean distance ratio. Experiments show
it has a good result, comparisons with SIFT and SURF is made to prove its performance.
In this paper a face-recognition algorithm with a confidence evaluation function for batch of SIFT feature is presented.
Confidence evaluation function is rarely used in traditional face recognition, which is an important index in future
recognition. In our face-recognition algorithm, two main steps are provided, that is primary election and strict
identification. Adaboost algorithm can detect rough features to collect candidate face regions, it works as primary
election algorithm. SIFT can describe the detail features in the face regions, the confidence evaluation function for batch
of SIFT feature is highly distinctive, and it work as strict identification algorithm. This confidence evaluation function is
a reliable measurement for matching multi-candidate regions containing invariant features. And, it can also be used in
image retrieval, object recognition.
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