Segmentation and labeling of face images for electronic documents

Face image segmentation and labeling is required in several quality tests which a face image has to pass in order to be included into an electronic ID document. The complexity of such a problem depends on the complexity of the scene, but in general there are no restrictions to the scene. The procedure that we have developed segments a face image into five regions: skin, hair, shoulders, background and padding frame. The presented method consists of two main steps: oversegmentation and labeling. In the first step, the image is segmented into homogeneous regions, whereas in the second step, the labeling of the homogeneous regions is performed. In the course of our research we experimented with several methods for the two described steps, and in this paper we present a setup in which the oversegmentation is performed using the mean-shift segmentation, and labeling is performed using the AdaBoost classification algorithm. Such setup has produced the best results in our experiments which we also present herein.

► Segmentation and labeling of facial images for later quality tests. ► Segmentation based on mean-shift method, labeling based on machine learning method. ► Complex texture features used for labeling. ► Manual segmentation results used as ground truth introduce significant variability.