A Survey of Synthetic Biometrics: Capabilities and Benefits

Within the Association for Computing Machinery (ACM) Special Interest Group on Computer Graphics (SIGGRAPH) community, a long-standing goal has been held for photo-realism in the generation of synthetic images—a goal that some feel has been achieved [7]. This body of work, spanning over three decades, documents achievements in modeling, animation, and rendering human subjects. The visual products range from feature films, commercial art, to video games. Computer generated “synthetic” biometrics are not widely used within the biometrics community beyond their current use as a research tool. Yet they offer a number of potential advantages that can be developed further to support the science and practical use of biometrics. They can be used to improve the understanding of a biometric system’s robustness and as an engineering tool to predict system performance. This paper surveys the state of synthetic biometrics generation, provides a glimpse at some benefits that can be obtained from their use, and discusses the issues retarding their adoption by the biometrics community. The most advanced synthetic images come from the movie industry and have yet to be fully adapted to suit biometric needs. A synthetically rendered face is illustrated in Figure 1.

[1]  Ulrich Neumann,et al.  Interactive multiresolution hair modeling and editing , 2002, SIGGRAPH.

[2]  Richard Saucier,et al.  Computer Generation of Statistical Distributions , 2000 .

[3]  Zoran Popovic,et al.  The space of human body shapes: reconstruction and parameterization from range scans , 2003, ACM Trans. Graph..

[4]  Nicholas M. Orlans,et al.  Parametrically controlled synthetic imagery experiment for face recognition testing , 2003, WBMA '03.

[5]  Anil K. Jain,et al.  FVC2002: Second Fingerprint Verification Competition , 2002, Object recognition supported by user interaction for service robots.

[6]  Dario Maio,et al.  Synthetic fingerprint-image generation , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[7]  Joe Marques,et al.  Effects of Eye Position on Eigenface-Based Face Recognition Scoring , 2003 .

[8]  N. Badler,et al.  Eyes Alive Eyes Alive Eyes Alive Figure 1: Sample Images of an Animated Face with Eye Movements , 2022 .

[9]  John Daugman,et al.  The importance of being random: statistical principles of iris recognition , 2003, Pattern Recognit..

[10]  Paul E. Debevec,et al.  Acquiring the reflectance field of a human face , 2000, SIGGRAPH.

[11]  Chris J. Hill,et al.  Risk of Masquerade Arising from the Storage of Biometrics , 2001 .

[12]  Petros Faloutsos,et al.  Composable controllers for physics-based character animation , 2001, SIGGRAPH.

[13]  Aaron F. Bobick,et al.  Using similarity scores from a small gallery to estimate recognition performance for larger galleries , 2003, 2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443).

[14]  Matthew Turk,et al.  A Morphable Model For The Synthesis Of 3D Faces , 1999, SIGGRAPH.

[15]  Dimitris N. Metaxas,et al.  Automating gait generation , 2001, SIGGRAPH.

[16]  Douglas J. Buettner.  Effects of WSQ Compression on Client-server Biometric Identification Performance , 2002, IC-AI.

[17]  Erik Reinhard,et al.  An Ocularist's Approach to Human Iris Synthesis , 2003, IEEE Computer Graphics and Applications.

[18]  Aaron F. Bobick,et al.  Predicting Large Population Data Cumulative Match Characteristic Performance from Small Population Data , 2003, AVBPA.

[19]  Anil K. Jain,et al.  Handbook of Fingerprint Recognition , 2005, Springer Professional Computing.

[20]  Jon Sporring,et al.  Statistical variation of Three Dimensional face models , 2003 .

[21]  James Arvo,et al.  A framework for realistic image synthesis , 1997, SIGGRAPH.

[22]  Anil K. Jain,et al.  FVC2000: Fingerprint Verification Competition , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Sébastien de Sainte-Croix.  Los Angeles, 1999 , 2003 .

[24]  J. L. Wayman,et al.  Best practices in testing and reporting performance of biometric devices. , 2002 .

[25]  Sharath Pankanti,et al.  On the Individuality of Fingerprints , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  David S. Ebert,et al.  Advanced modeling techniques for computer graphics , 1996, CSUR.

[27]  Alice J. O'Toole,et al.  Face recognition algorithms and the other-race effect: computational mechanisms for a developmental contact hypothesis , 2002, Cogn. Sci..

[28]  Keith Waters,et al.  A muscle model for animation three-dimensional facial expression , 1987, SIGGRAPH.

[29]  P. Jonathon Phillips,et al.  Face Recognition Vendor Test 2002: Overview and Summary , 2003 .

[30]  K. Mardia,et al.  Statistical Shape Analysis , 1998 .

[31]  Jie Yan,et al.  Realistic Virtual Face and Body Synthesis , 2000, MVA.

[32]  Yitao Duan.  Fuzzy Role Based Expert System for Human Thermoregulation Model ME 290 M Final Project Spring 1999 , 1999 .