Cross-Age Face Recognition on a Very Large Database: The Performance versus Age Intervals and Improvement Using Soft Biometric Traits

Facial aging can degrade the face recognition performance dramatically. Traditional face recognition studies focus on dealing with pose, illumination, and expression (PIE) changes. Considering a large span of age difference, the influence of facial aging could be very significant compared to the PIE variations. How big the aging influence could be? What is the relation between recognition accuracy and age intervals? Can soft biometrics be used to improve the face recognition performance under age variations? In this paper we address all these issues. First, we investigate the face recognition performance degradation with respect to age intervals between the probe and gallery images on a very large database which contains about 55,000 face images of more than 13,000 individuals. Second, we study if soft biometric traits, e.g., race, gender, height, and weight, could be used to improve the cross-age face recognition accuracies, and how useful each of them could be.

[1]  Gian Luca Marcialis,et al.  Group-specific face verification using soft biometrics , 2009, J. Vis. Lang. Comput..

[2]  Stefano Soatto,et al.  A Study of Face Recognition as People Age , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[3]  I. G. Priest.  THE OPTICAL SOCIETY OF AMERICA. , 1940, Science.

[4]  Timothy F. Cootes,et al.  Toward Automatic Simulation of Aging Effects on Face Images , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Zhi-Hua Zhou,et al.  Automatic Age Estimation Based on Facial Aging Patterns , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Anil K. Jain,et al.  Integrating Faces, Fingerprints, and Soft Biometric Traits for User Recognition , 2004, ECCV Workshop BioAW.

[7]  L Sirovich,et al.  Low-dimensional procedure for the characterization of human faces. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[8]  Karl Ricanek,et al.  MORPH: a longitudinal image database of normal adult age-progression , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[9]  Bruce A. Draper,et al.  The CSU Face Identification Evaluation System: Its Purpose, Features, and Structure , 2003, ICVS.

[10]  Norbert Krüger,et al.  Face recognition by elastic bunch graph matching , 1997, Proceedings of International Conference on Image Processing.

[11]  Yiying Tong,et al.  Face recognition with temporal invariance: A 3D aging model , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[12]  Anil K. Jain,et al.  Soft Biometric Traits for Personal Recognition Systems , 2004, ICBA.

[13]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression Database , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Rama Chellappa,et al.  Modeling Age Progression in Young Faces , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[15]  K. Ricanek,et al.  AUTOMATIC REPRESENTATION OF ADULT AGING IN FACIAL IMAGES , 2022 .

[16]  Xinggang Lin,et al.  Age simulation for face recognition , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

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

[18]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.