Automatic Face Recognition System Based on Local Fourier-Bessel Features

We present an automatic face verification system inspired by known properties of biological systems. In the proposed algorithm the whole image is converted from the spatial to polar frequency domain by a Fourier-Bessel Transform (FBT). Using the whole image is compared to the case where only face image regions (local analysis) are considered. The resulting representations are embedded in a dissimilarity space, where each image is represented by its distance to all the other images, and a Pseudo-Fisher discriminator is built. Verification test results on the FERET database showed that the local-based algorithm outperforms the global-FBT version. The local-FBT algorithm performed as state-of-the-art methods under different testing conditions, indicating that the proposed system is highly robust for expression, age, and illumination variations. We also evaluated the performance of the proposed system under strong occlusion conditions and found that it is highly robust for up to 50% of face occlusion. Finally, we automated completely the verification system by implementing face and eye detection algorithms. Under this condition, the local approach was only slightly superior to the global approach.Comment: 2005, Brazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI

A Distributed Weighted Voting Approach for Accurate Eye Center Estimation

This paper proposes a novel approach for accurate estimation of eye center in face images. A distributed voting based approach in which every pixel votes is adopted for potential eye center candidates. The votes are distributed over a subset of pixels which lie in a direction which is opposite to gradient direction and the weightage of votes is distributed according to a novel mechanism.  First, image is normalized to eliminate illumination variations and its edge map is generated using Canny edge detector. Distributed voting is applied on the edge image to generate different eye center candidates. Morphological closing and local maxima search are used to reduce the number of candidates. A classifier based on spatial and intensity information is used to choose the correct candidates for the locations of eye center. The proposed approach was tested on BioID face database and resulted in better Iris detection rate than the state-of-the-art. The proposed approach is robust against illumination variation, small pose variations, presence of eye glasses and partial occlusion of eyes.Defence Science Journal, 2013, 63(3), pp.292-297, DOI:http://dx.doi.org/10.14429/dsj.63.276

Unobtrusive and pervasive video-based eye-gaze tracking

Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe

Towards an efficient, unsupervised and automatic face detection system for unconstrained environments

Nowadays, there is growing interest in face detection applications for unconstrained environments. The increasing need for public security and national security motivated our research on the automatic face detection system. For public security surveillance applications, the face detection system must be able to cope with unconstrained environments, which includes cluttered background and complicated illuminations. Supervised approaches give very good results on constrained environments, but when it comes to unconstrained environments, even obtaining all the training samples needed is sometimes impractical. The limitation of supervised approaches impels us to turn to unsupervised approaches. In this thesis, we present an efficient and unsupervised face detection system, which is feature and configuration based. It combines geometric feature detection and local appearance feature extraction to increase stability and performance of the detection process. It also contains a novel adaptive lighting compensation approach to normalize the complicated illumination in real life environments. We aim to develop a system that has as few assumptions as possible from the very beginning, is robust and exploits accuracy/complexity trade-offs as much as possible. Although our attempt is ambitious for such an ill posed problem-we manage to tackle it in the end with very few assumptions.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Monokulare Blickrichtungsschätzung zur berührungslosen Mensch-Maschine-Interaktion

Die vorliegende Arbeit beschäftigt sich mit der berührungslosen Mensch-Maschine-Interaktion, welche hier als Interaktion mittels Erkennen der Blickrichtung des Nutzers unter Verwendung einfacher Hardware interpretiert wird. Die Forschungsschwerpunkte liegen in der Extraktion der zur Bestimmung der Blickrichtung benötigten Informationen aus 2D-Bilddaten, bestehend aus der präzisen Position der Iriden und der dreidimensionalen Position des Kopfes, mittels derer die Blickrichtung bestimmt wird

Monokulare Blickrichtungsschätzung zur berührungslosen Mensch-Maschine-Interaktion

Die vorliegende Arbeit beschäftigt sich mit der berührungslosen Mensch-Maschine-Interaktion, welche hier als Interaktion mittels Erkennen der Blickrichtung des Nutzers unter Verwendung einfacher Hardware interpretiert wird. Die Forschungsschwerpunkte liegen in der Extraktion der zur Bestimmung der Blickrichtung benötigten Informationen aus 2D-Bilddaten, bestehend aus der präzisen Position der Iriden und der dreidimensionalen Position des Kopfes, mittels derer die Blickrichtung bestimmt wird

Detection of Eye Locations in Unconstrained Visual Images

This paper describes a computational approach for accurately determining the location of human eyes in unconstrained monoscopic gray level images. The proposed method is based on exploiting the flow field characteristics that arise due to the presence of a dark iris surrounded by a light sclera. A novel aspect of the proposed method lies in its use of both spatial and temporal information to detect the location of the eyes. The spatial processing utilizes flow field information to select a pool of potential candidate locations for the eyes. Temporal processing uses the principle of continuity to filter out the actual location of the eyes from the pool of potential candidates. Extensions for gaze angle determination, and the tracking of human point-of-regard are indicated