A comparison of face and facial feature detectors based on the viola-jones general object detection framework

[EN]The human face provides useful information during interaction; therefore, any system integrating Vision- BasedHuman Computer Interaction requires fast and reliable face and facial feature detection. Different approaches have focused on this ability but only open source implementations have been extensively used by researchers. A good example is the Viola–Jones object detection framework that particularly in the context of facial processing has been frequently used.

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.

Face and eye detection on hard datasets

[EN] This paper analyzes the detection and localization performance of the participating face and eye algorithms compared with the Viola Jones detector and four leading commercial face detectors. Performance is characterized under the different conditions and parameterized by per-image brightness and contrast. In localization accuracy for eyes, the groups/companies focusing on long-range face detection outperform leading commercial applications.

Combining face and facial feature detectors for face detection performance improvement

[EN]In this paper, we experimentally study the combination of face and facial feature detectors to improve face detection performance. The face detection problem, as suggeted by recent face detection challenges, is still not solved. Face detectors traditionally fail in large-scale problems and/or when the face is occluded or di erent head rotations are present. The combination of face and facial feature detectors is evaluated with a public database. The obtained results evidence an improvement in the positive detection rate while reducing the false detection rate. Additionally, we prove that the integration of facial feature detectors provides useful information for pose estimation and face alignment.

Comparison of focus measures in face detection environments

[EN]This work presents a comparison among different focus measures used in the literature for autofocusing in a non previously explored application of face detection. This application has different characteristics to those where traditionally autofocus methods have been applied like microscopy or depth from focus. The aim of the work is to find if the best focus measures in traditional applications of autofocus have the same performance in face detection applications. To do that six focus measures has been studied in four different settings from the oldest to more recent ones.

Face recognition from a tabula rasa perspective

[EN]In this paper a system for face recognition from a tabula rasa (i.e. blank slate) perspective is described. A priori, the system has the only ability to detect automatically faces and represent them in a space of reduced dimension. Later, the system is exposed to over 400 different identities, observing its recognition performance evolution. The preliminary results achieved indicate on the one side that the system is able to reject most of unknown individuals after an initialization stage.