Changes in human facial characteristics over time are inevitable. Hence, it is not surprising that face matching accuracy decreases with increasing elapsed time between two face images of the same person. With the deployment of automatic face recognition systems for de-duplication and law enforcement applications, it is crucial that we gain a better understanding of how facial aging due to time lapse affects recognition performance. Some people "age well" while the facial appearance of others changes drastically over time; this disparity mandates a subject-specific aging analysis. In this ongoing project, we use multilevel statistical models on a longitudinal database of 147,784 face images of 18,007 subjects (see Figs. 1 and 2) to quantify how time lapse affects match scores and recognition accuracy. A similar approach was used by Yoon and Jain [1] for fingerprint analysis and by Grother et al. [2] for iris analysis. Using two state-of-the-art COTS face matchers, we report (i) population-mean temporal trends in genuine scores and (ii) inter-subject variations, (iii) effects of other covariates (gender, race, face quality), and (iv) probability of true acceptance over time. Our preliminary findings suggest that false rejection rates of one of the COTS matchers remain stable up to approximately 10 years time interval.
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| Fig. 1 Face images and corresponding ages (in years) of three example subjects from the PCSO_LS database. |
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| Fig. 2 Properties of the longitudinal database of face images (mug shots) used in this study: (a) number of face images per subject, (b) elapsed time between the first and last image of each subject, and (c) gender (male or female) and race (white or black) distribution. In total, there are 147,784 images of 18,007 subjects in the database (PCSO_LS). |
Relevant Publication(s)
1. L. Best-Rowden and A. K. Jain, "A Longitudinal Study of Automatic Face Recognition", ICB, Phuket, Thailand, May 19-22, 2015. [pdf]
References
[1] S. Yoon and A. K. Jain, "Longitudinal Study of Fingerprint Recognition", Tech. Report MSU-CSE-14-3, Michigan State University, East Lansing, MI, USA, Jun. 2014.
[2] P. Grother, J. R. Matey, E. Tabassi, G.W. Quinn, and M. Chumakov. IREX VI: Temporal Stability of Iris Recognition Accuracy. NIST Interagency Report 7948, Jul. 2013.
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