Face-based recognition techniques: proposals for the metrological characterization of global and feature-based approaches

Security systems based on face recognition through video surveillance systems deserve great interest. Their use is important in several areas including airport security, identification of individuals and access control to critical areas. These systems are based either on the measurement of details of a human face or on a global approach whereby faces are considered as a whole. The recognition is then performed by comparing the measured parameters with reference values stored in a database. The result of this comparison is not deterministic because measurement results are affected by uncertainty due to random variations and/or to systematic effects. In these circumstances the recognition of a face is subject to the risk of a faulty decision. Therefore, a proper metrological characterization is needed to improve the performance of such systems. Suitable methods are proposed for a quantitative metrological characterization of face measurement systems, on which recognition procedures are based. The proposed methods are applied to three different algorithms based either on linear discrimination, on eigenface analysis, or on feature detection.

[1]  Hans-Peter Seidel,et al.  Rapid stereo-vision enhanced face recognition , 2010, 2010 IEEE International Conference on Image Processing.

[2]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Cairong Zou,et al.  Face recognition using common faces method , 2006, Pattern Recognit..

[4]  Alfredo Paolillo,et al.  A structured approach for the metrological characterization of biometric systems based on face recognition , 2010 .

[5]  Josef Kittler,et al.  Independent component analysis in a local facial residue space for face recognition , 2004, Pattern Recognit..

[6]  Giovanni Battista Rossi.  A probabilistic model for measurement processes , 2003 .

[7]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Hyeonjoon Moon,et al.  A verification protocol and statistical performance analysis for face recognition algorithms , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[9]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[10]  M. A. Grudin,et al.  On internal representations in face recognition systems , 2000, Pattern Recognit..

[11]  A. Cigada,et al.  Personal Identification Through 3D Biometric Measurements Based on Stereoscopic Image Pairs , 2006, 2006 IEEE International Workshop on Measurement Systems for Homeland Security, Contraband Detection and Personal Safety.

[12]  Tomaso A. Poggio,et al.  Face recognition: component-based versus global approaches , 2003, Comput. Vis. Image Underst..

[13]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[14]  Li Wei,et al.  3D Face Recognition Using Face Feature Points Based on Parallel Stereo Vision , 2010, J. Digit. Content Technol. its Appl..

[15]  Giovanni Battista Rossi,et al.  METROLOGY SOFTWARE FOR THE EXPRESSION OF MEASUREMENT RESULTS BY DIRECT CALCULATION OF PROBABILITY DISTRIBUTIONS , 2004 .

[16]  K. Etemad,et al.  Discriminant analysis for recognition of human face images , 1997 .

[17]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.

[18]  Bruce A. Draper,et al.  Recognizing faces with PCA and ICA , 2003, Comput. Vis. Image Underst..