The ENCARA system for face detection and normalization

[EN]Facial image processing is becoming widespread in human-computer applications, despite its complexity. High-level processes such as face recognition or gender determination rely on low-level routines that must e ectively detect and normalize the faces that appear in the input image. In this paper, a face detection and normalization system is described. The approach taken is based on a cascade of fast, weak classi ers that together try to determine whether a frontal face is present in the image.

An study on ear detection and its applications to face detection

[EN]OpenCV includes di erent object detectors based on the Viola-Jones framework. Most of them are specialized to deal with the frontal face pattern and its inner elements: eyes, nose, and mouth. In this paper, we focus on the ear pattern detection, particularly when a head pro le or almost pro le view is present in the image. We aim at creating real-time ear detectors based on the general object detection framework provided with OpenCV. After training classi ers to detect left ears, right ears, and ears in general, the performance achieved is valid to be used to feed not only a head pose estimation system but also other applications such as those based on ear biometrics.

Gender and identity classification for a naive and evolving system

[EN]This paper does not propose a new technique for face representationorclassification. Insteadtheworkdescribed here investigates the evolution of an automatic system which, based on a currently common framework, and starting from an empty memory, modifies its classifiers according to experience. In the experiments we reproduce up to a certain extent the process of successive meetings. The results achieved, even when the number of different individuals is still reduced compared to off-line classifiers, are promising.