Detection of Facial Features in Scale-Space
DOI:
https://doi.org/10.14311/948Keywords:
clustering methods, face recognition, feature extraction, interest points, Karhunen-Loeve transforms, object detection, pattern classificationAbstract
This paper presents a new approach to the detection of facial features. A scale adapted Harris Corner detector is used to find interest points in scale-space. These points are described by the SIFT descriptor. Thus invariance with respect to image scale, rotation and illumination is obtained. Applying a Karhunen-Loeve transform reduces the dimensionality of the feature space. In the training process these features are clustered by the k-means algorithm, followed by a cluster analysis to find the most distinctive clusters, which represent facial features in feature space. Finally, a classifier based on the nearest neighbor approach is used to decide whether the features obtained from the interest points are facial features or not.Downloads
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