Aspects of Age Variation in Facial Morphology Affecting Biometrics

Patterson, Eric; Sethuram, Amrutha; Albert, Midori; Ricanek, Karl; King, Michael C.

doi:10.1109/btas.2007.4401953

Cited by 73 publications

(31 citation statements)

References 25 publications

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“…The brighter the value in the picture, the faster the temporal evolution of the point. These results are in line with the qualitative results reported in [10].…”

Section: Experimental Evaluationsupporting

confidence: 93%

“…As for the former, it involves gradual and deep modifications in the face structure and texture. These can be accounted for reduction in the fat tissue, progressive degradation of the skin elasticity, reduction of the tone of the facial muscles, development of the skull bones and non uniform pigmentation of the skin [10]. The short time effects can be either due to the same phenomena as well as to other, even transient, effects such as the change in facial tan, hair and beard growth, or small textural changes in the skin.…”

Section: Introductionmentioning

confidence: 99%

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On the Quantitative Estimation of Short-Term Aging in Human Faces

Brodo

Bicego

Tistarelli

2009

Image Analysis and Processing – ICIAP 2009

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Abstract. Facial aging has been only partially studied in the past and mostly in a qualitative way. This paper presents a novel approach to the estimation of facial aging aimed to the quantitative evaluation of the changes in facial appearance over time. In particular, the changes both in face shape and texture, due to short-time aging, are considered. The developed framework exploits the concept of "distinctiveness" of facial features and the temporal evolution of such measure. The analysis is performed both at a global and local level to define the features which are more stable over time.Several experiments are performed on publicly available databases with image sequences densely sampled over a time span of several years. The reported results clearly show the potential of the methodology to a number of applications in biometric identification from human faces.

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“…The brighter the value in the picture, the faster the temporal evolution of the point. These results are in line with the qualitative results reported in [10].…”

Section: Experimental Evaluationsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

On the Quantitative Estimation of Short-Term Aging in Human Faces

Brodo

Bicego

Tistarelli

2009

Image Analysis and Processing – ICIAP 2009

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“…The MORPH database consists of two albums, which, in total, contains roughly 100,000 images of about 25,000 subjects. The MORPH dataset has facilitated studies on synthetic aging [16,22], age invariant face recognition [11,12,16], age estimation [5], and aging analysis [17]. A broader examination of facial aging methods in the literature can be found in the summary provided by Ramanathan et al [19].…”

Section: Introductionmentioning

confidence: 99%

Face recognition across time lapse: On learning feature subspaces

Klare

Jain

2011

2011 International Joint Conference on Biometrics (IJCB)

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There is a growing interest in understanding the impact of aging on face recognition performance, as well as designing recognition algorithms that are mostly invariant to temporal changes. While some success has been made on this front, a fundamental questions has yet to be answered: do face recognition systems that compensate for the effects of aging compromise recognition performance for faces that have not undergone any aging? The studies in this paper help confirm that age invariant systems do seem to decrease performance in non-aging scenarios. This is demonstrated by performing training experiments on the largest face aging dataset studied in the literature to date (over 200,000 images from roughly 64,000 subjects). Further experiments conducted in this research help demonstrate the impact of aging on two leading commercial face recognition systems. We also determine the regions of the face that remain the most stable over time.

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“…introducing aging by caricatures generated by shifting 3D model parameters [12]. Patternson et al [15] compared automatic aging simulation results with forensic sketches and showed that further studies in aging are needed to improve face recognition techniques. A few image-based approaches in 2D have already been proposed to simulate both growth and adult aging, e.g.…”

Section: Related Workmentioning

confidence: 99%