Abstract-As face recognition applications progress from constrained sensing and cooperative subjects scenarios (e.g., driver's license and passport photos) to unconstrained scenarios with uncooperative subjects (e.g., video surveillance), new challenges are encountered. These challenges are due to variations in ambient illumination, image resolution, background clutter, facial pose, expression, and occlusion. In forensic investigations where the goal is to identify a "person of interest," often based on low quality face images and videos, we need to utilize whatever source of information is available about the person. This could include one or more video tracks, multiple still images captured by bystanders (using, for example, their mobile phones), 3D face models constructed from image(s) and video(s), and verbal descriptions of the subject provided by witnesses. These verbal descriptions can be used to generate a face sketch and provide ancillary information about the person of interest (e.g., gender, race, and age). While traditional face matching methods generally take a single media (i.e., a still face image, video track, or face sketch) as input, our work considers using the entire gamut of media as a probe to generate a single candidate list for the person of interest. We show that the proposed approach boosts the likelihood of correctly identifying the person of interest through the use of different fusion schemes, 3D face models, and incorporation of quality measures for fusion and video frame selection.

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With the deployment of automatic face recognition systems for many large-scale applications, it is crucial that we gain a thorough understanding of how facial aging affects the recognition performance, particularly across a large population. Because aging is a complex process involving genetic and environmental factors, some faces "age well" while the appearance of others changes drastically over time. This heterogeneity (inter-subject variability) suggests the need for a subject-specific aging analysis. In this paper, we conduct such an analysis using a longitudinal database of 147,784 operational mug shots of 18,007 repeat criminal offenders, where each subject has at least five face images acquired over a minimum of five years. By fitting multilevel statistical models to genuine similarity scores from two commercial-off-the-shelf (COTS) matchers, we quantify (i) the population average rate of change in genuine scores with respect to the elapsed time between two face images, and (ii) how closely the subject-specific rates of change follow the population average. Longitudinal analysis of the scores from the more accurate COTS matcher shows that despite decreasing genuine scores over time, the average subject can still be correctly verified at a false accept rate (FAR) of 0.01% across all 16 years of elapsed time in our database. We also investigate (i) the effects of several other covariates (gender, race, face quality), and (ii) the probability of true acceptance over time.

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The two underlying premises of automatic face recognition are uniqueness and permanence. This paper investigates the permanence property by addressing the following: Does face recognition ability of state-of-the-art systems degrade with elapsed time between enrolled and query face images? If so, what is the rate of decline w.r.t. the elapsed time? While previous studies have reported degradations in accuracy, no formal statistical analysis of large-scale longitudinal data has been conducted. We conduct such an analysis on two mugshot databases, which are the largest facial aging databases studied to date in terms of number of subjects, images per subject, and elapsed times. Mixed-effects regression models are applied to genuine similarity scores from state-of-the-art COTS face matchers to quantify the population-mean rate of change in genuine scores over time, subject-specific variability, and the influence of age, sex, race, and face image quality. Longitudinal analysis shows that despite decreasing genuine scores, 99% of subjects can still be recognized at 0.01% FAR up to approximately 6 years elapsed time, and that age, sex, and race only marginally influence these trends. The methodology presented here should be periodically repeated to determine age-invariant properties of face recognition as state-of-the-art evolves to better address facial aging.