3D Face Recognition: Feature Extraction Based on Directional Signatures from Range Data and Disparity Maps

In this paper, the author presents a work on i) range data and ii) stereo-vision system based disparity map profiling that are used as signatures for 3D face recognition. The signatures capture the intensity variations along a line at sample points on a face in any particular direction. The directional signatures and some of their combinations are compared to study the variability in recognition performances. Two 3D face image datasets namely, a local student database captured with a stereo vision system and the FRGC v1 range dataset are used for performance evaluation

Setting a world record in 3D face recognition

Biometrics - recognition of persons based on how they look or behave, is the main subject of research at the Chair of Biometric Pattern Recognition (BPR) of the Services, Cyber Security and Safety Group (SCS) of the EEMCS Faculty at the University of Twente. Examples are finger print recognition, iris and face recognition. A relatively new field is 3D face recognition based on the shape of the face rather that its appearance. This paper presents a method for 3D face recognition developed at the Chair of Biometric Pattern Recognition (BPR) of the Services, Cyber Security and Safety Group (SCS) of the EEMCS Faculty at the University of Twente and published in 2011. The paper also shows that noteworthy performance gains can be obtained by optimisation of an existing method. The method is based on registration to an intrinsic coordinate system using the vertical symmetry plane of the head, the tip of the nose and the slope of the nose bridge. For feature extraction and classification multiple regional PCA-LDA-likelihood ratio based classifiers are fused using a fixed FAR voting strategy. We present solutions for correction of motion artifacts in 3D scans, improved registration and improved training of the used PCA-LDA classifier using automatic outlier removal. These result in a notable improvement of the recognition rates. The all vs all verification rate for the FRGC v2 dataset jumps to 99.3% and the identification rate for the all vs first to 99.4%. Both are to our knowledge the best results ever obtained for these benchmarks by a fairly large margin

Using 3D Representations of the Nasal Region for Improved Landmarking and Expression Robust Recognition

This paper investigates the performance of different representations of 3D human nasal region for expression robust recognition. By performing evaluations on the depth and surface normal components of the facial surface, the nasal region is shown to be relatively consistent over various expressions, providing motivation for using the nasal region as a biometric. A new efficient landmarking algorithm that thresholds the local surface normal components is proposed and demonstrated to produce an improved recognition performance for nasal curves from both the depth and surface normal components. The use of the Shape Index for feature extraction is also investigated and shown to produce a good recognition performance

Using 3D Representations of the Nasal Region for Improved Landmarking and Expression Robust Recognition

This paper investigates the performance of different representations of 3D human nasal region for expression robust recognition. By performing evaluations on the depth and surface normal components of the facial surface, the nasal region is shown to be relatively consistent over various expressions, providing motivation for using the nasal region as a biometric. A new efficient landmarking algorithm that thresholds the local surface normal components is proposed and demonstrated to produce an improved recognition performance for nasal curves from both the depth and surface normal components. The use of the Shape Index for feature extraction is also investigated and shown to produce a good recognition performance

Fusion d'Experts pour une Biométrie Faciale 3D Robuste aux Déformations

Session "Posters"National audienceNous étudions dans cet article l'apport de la géométrie tridimensionnelle du visage dans la reconnaissance des individus. La principale contribution est d'associer plusieurs experts (matcheurs) de biométrie faciale 3D afin d'achever de meilleures performances comparées aux performances individuelles de chacun, notamment en présence d'expressions. Les experts utilisés sont : (E1) Courbes radiales élastiques, (E2) MS-eLBP, une version étendue multi-échelle de l'opérateur LBP, (E3) l'algorithme de recalage non-rigide TPS, en plus d'un expert de référence (Eref) l'algorithme de recalage rigide connu ICP. Profitant de la complémentarité de chacun des experts, la présente approche affiche un taux d'identification qui dépasse les 99% en présence d'expressions faciales sur la base FRGCv2. Une étude comparative avec l'état de l'art confirme le choix et l'intérêt de combiner plusieurs experts afin d'achever de meilleurs performance