UMB-DB: A database of partially occluded 3D faces

In this paper we present the UMB-DB 3D face database. The database has been built to test algorithms and systems for 3D face analysis in uncontrolled and challenging scenarios, in particular in those cases where faces are occluded. The database is composed of 1473 pairs of depth and color images of 143 subjects. Each subject has been acquired with different facial expressions, and with the face partially occluded by various objects such as eyeglasses, hats, scarves and hands. The total number of occluded acquisitions is 578. The database, that is freely available for research purposes, could be used for various investigations, some of which are suggested in the paper. For the sake of comparison, we report the results of some of the 3D face detection and recognition algorithms in the state of the art.

[1]  Patrick J. Flynn,et al.  An evaluation of multimodal 2D+3D face biometrics , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Cristina Conde,et al.  3D Facial Normalization with Spin Images and Influence of Range Data Calculation over Face Verification , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[3]  J. Vélez,et al.  Face recognition using 3D local geometrical features: PCA vs. SVM , 2005, ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005..

[4]  Sang Uk Lee,et al.  Face recognition using face-ARG matching , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Jun Wang,et al.  A 3D facial expression database for facial behavior research , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[6]  R. Schettini,et al.  A 3D face recognition system using curvature-based detection and holistic multimodal classification , 2005, ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005..

[7]  Raimondo Schettini,et al.  Gappy PCA Classification for Occlusion Tolerant 3D Face Detection , 2009, Journal of Mathematical Imaging and Vision.

[8]  Arman Savran,et al.  Bosphorus Database for 3D Face Analysis , 2008, BIOID.

[9]  Aleix M. Martínez,et al.  Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Aleix M. Martínez,et al.  Recognition of partially occluded and/or imprecisely localized faces using a probabilistic approach , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[12]  Andrea Cavallaro,et al.  3-D Face Detection, Landmark Localization, and Registration Using a Point Distribution Model , 2009, IEEE Transactions on Multimedia.

[13]  Marc Acheroy,et al.  Face verification from 3D and grey level clues , 2001, Pattern Recognit. Lett..

[14]  Xun Xu,et al.  Building Large Scale 3D Face Database for Face Analysis , 2007, MCAM.

[15]  L. Akarun,et al.  How to deceive a face recognizer ? , 2004 .

[16]  Dahua Lin,et al.  Quality-Driven Face Occlusion Detection and Recovery , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Alberto Del Bimbo,et al.  Description and retrieval of 3D face models using iso-geodesic stripes , 2006, MIR '06.

[18]  Wen Gao,et al.  Local Gabor Binary Patterns Based on Kullback–Leibler Divergence for Partially Occluded Face Recognition , 2007, IEEE Signal Processing Letters.

[19]  John P. Lewis,et al.  Face Inpainting with Local Linear Representations , 2004, BMVC.

[20]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[21]  R. Gross.  Face Databases , 2005 .

[22]  Luc Van Gool,et al.  A Generalized EM Approach for 3D Model Based Face Recognition under Occlusions , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[23]  F. Tarres,et al.  A novel method for face recognition under partial occlusion or facial expression variations , 2005, 47th International Symposium ELMAR, 2005..

[24]  Raimondo Schettini,et al.  Three-Dimensional Occlusion Detection and Restoration of Partially Occluded Faces , 2011, Journal of Mathematical Imaging and Vision.

[25]  Jongsun Kim,et al.  Effective representation using ICA for face recognition robust to local distortion and partial occlusion , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Jim Austin,et al.  Three-dimensional face recognition using combinations of surface feature map subspace components , 2008, Image Vis. Comput..

[27]  Patrick J. Flynn,et al.  Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[28]  Tsuhan Chen,et al.  Biometrics : Challenges arising from Theory to Practice , 2004 .

[29]  Tieniu Tan,et al.  Robust 3D Face Recognition Using Learned Visual Codebook , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  L. Akarun,et al.  A 3D Face Recognition System for Expression and Occlusion Invariance , 2008, 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems.

[31]  Sang Chul Ahn,et al.  Glasses removal from facial image using recursive error compensation , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Danijel Sko.  Robust Recognition and Pose Determination of 3-D Objects Using Range Images in Eigenspace Approach ⁄ , 2001 .

[33]  Seong-Whan Lee,et al.  Reconstruction of Partially Damaged Face Images Based on a Morphable Face Model , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Raimondo Schettini,et al.  3D face detection using curvature analysis , 2006, Pattern Recognit..

[35]  Raimondo Schettini,et al.  Face^3 a 2D+3D Robust Face Recognition System , 2007, 14th International Conference on Image Analysis and Processing (ICIAP 2007).

[36]  K.W. Bowyer,et al.  Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.

[37]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[38]  Michael M. Bronstein.  Expression-invariant 3D face recognition , 2008 .

[39]  Alan C. Bovik,et al.  Texas 3D Face Recognition Database , 2010, 2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI).