Relative Spatial Weighting of Features for Localizing Parts of Faces

This paper proposes an approach for detecting important parts of faces in uncontrolled imaging settings. Regions of special interest in faces of humans are eyes and eyebrows, nose and mouth. The approach works by first extracting ORB (Oriented FAST and Rotated BRIEF) and SURF (Speeded up robust features) features, secondly a supervised learning step with a random subset of images is performed using k-means algorithm for devising the clusters’ centers of the important parts of faces. For the testing set of images the normalized values of each new ORB or SURF feature is weighted positively depending on its similarity and proximity of a cluster center (a face part). Tests were performed using the BioID dataset which consists of 1521 images of 23 different subjects in a variety of situations. Results show that the use of ORB features for face parts localization is more efficient and more precise than SIFT or SURF features alone. Also, the relative spatial weighting of a combination of ORB and SURF features enhances the localization of parts of faces.