Learning the Detection of Faces in Natural Images
نویسندگان
چکیده
We present a two-stage face-finding system as a combination of labeled graph matching and statistical learning. The data format for both stages consists of vectors of the responses of Gabor wavelet filters. Graph matching is used to detect possible locations of faces that we call hypotheses. These typically contain many false positives. The graphs at the found locations are then reinterpreted as vectors, which can be used as input for different statistical learning methods. The methods used here are K-Nearest-Neighbour and the Support Vector Machine with the latter being more efficient.
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