2013 |
Križaj, Janez; Dobrišek, Simon; Štruc, Vitomir; Pavešić, Nikola Robust 3D face recognition using adapted statistical models Proceedings Article In: Proceedings of the Electrotechnical and Computer Science Conference (ERK'13), 2013. Abstract | Links | BibTeX | Tags: 3d face recognition, biometrics, covariance descriptor, face verification, FRGC, GMM, modeling, performance evaluation, region-covariance matrix @inproceedings{krizajrobust, The paper presents a novel framework to 3D face recognition that exploits region covariance matrices (RCMs), Gaussian mixture models (GMMs) and support vector machine (SVM) classifiers. The proposed framework first combines several 3D face representations at the feature level using RCM descriptors and then derives low-dimensional feature vectors from the computed descriptors with the unscented transform. By doing so, it enables computations in Euclidean space, and makes Gaussian mixture modeling feasible. Finally, a support vector classifier is used for identity inference. As demonstrated by our experimental results on the FRGCv2 and UMB databases, the proposed framework is highly robust and exhibits desirable characteristics such as an inherent mechanism for data fusion (through the RCMs), the ability to examine local as well as global structures of the face with the same descriptor, the ability to integrate domain-specific prior knowledge into the modeling procedure and consequently to handle missing or unreliable data. |
Križaj, Janez; Štruc, Vitomir; Dobrišek, Simon Combining 3D face representations using region covariance descriptors and statistical models Proceedings Article In: Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition and Workshops (IEEE FG), Workshop on 3D Face Biometrics, IEEE, Shanghai, China, 2013. Abstract | Links | BibTeX | Tags: 3d face recognition, biometrics, covariance descriptors, face recognition, face verification, FG, gaussian mixture models, GMM, unscented transform @inproceedings{FG2013, The paper introduces a novel framework for 3D face recognition that capitalizes on region covariance descriptors and Gaussian mixture models. The framework presents an elegant and coherent way of combining multiple facial representations, while simultaneously examining all computed representations at various levels of locality. The framework first computes a number of region covariance matrices/descriptors from different sized regions of several image representations and then adopts the unscented transform to derive low-dimensional feature vectors from the computed descriptors. By doing so, it enables computations in the Euclidean space, and makes Gaussian mixture modeling feasible. In the last step a support vector machine classification scheme is used to make a decision regarding the identity of the modeled input 3D face image. The proposed framework exhibits several desirable characteristics, such as an inherent mechanism for data fusion/integration (through the region covariance matrices), the ability to examine the facial images at different levels of locality, and the ability to integrate domain-specific prior knowledge into the modeling procedure. We assess the feasibility of the proposed framework on the Face Recognition Grand Challenge version 2 (FRGCv2) database with highly encouraging results. |
2012 |
Križaj, Janez; Štruc, Vitomir; Dobrišek, Simon Robust 3D Face Recognition Journal Article In: Electrotechnical Review, vol. 79, no. 1-2, pp. 1-6, 2012. Abstract | Links | BibTeX | Tags: 3d face recognition, biometrics, gaussian mixture models, GMM, modeling @article{Križaj-EV-2012, Face recognition in uncontrolled environments is hindered by variations in illumination, pose, expression and occlusions of faces. Many practical face-recognition systems are affected by these variations. One way to increase the robustness to illumination and pose variations is to use 3D facial images. In this paper 3D face-recognition systems are presented. Their structure and operation are described. The robustness of such systems to variations in uncontrolled environments is emphasized. We present some preliminary results of a system developed in our laboratory. |