Abstract
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.
Highlights
Face detection is one of the most challenging tasks in object recognition, because of the high variance among human faces, including facial expression
This paper presents a new approach to the detection of facial features
A single cluster represents a distribution of a number of feature vectors f that belongs to the same facial feature
Summary
Face detection is one of the most challenging tasks in object recognition, because of the high variance among human faces, including facial expression. Several methods have been developed for face detection that deal with some but not all sources of variance Holistic approaches such as Eigenfaces [12] (sometimes called image-based [4] or appearance-based [14] methods in context of detection and recognition)or the boosted cascade of simple features [13] perform object detection by classifying image regions through a sliding window [15]. While they can be made invariant to scale and lighting, the major drawback of these techniques is that they cannot deal with different rotation or pose.
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