Face Recognition Performance under Aging

Deb, Debayan; Best-Rowden, Lacey; Jain, Anil K.

doi:10.1109/cvprw.2017.82

Cited by 29 publications

(27 citation statements)

References 18 publications

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“…Additionally, in Table 6 and Fig. 14, face verification confidence decreases as the time elapsed between two images increases, which conforms to the physical effect of face aging [27] [28]. It may also explain the better performance achieved on CACD in this evaluation, where the maximum mean age gap between the input and synthesized age cluster is 23.09 years, far less than that of 28.61 years achieved on MORPH.…”

Section: Experiments Ii: Aging Model Evaluationsupporting

confidence: 72%

Learning Continuous Face Age Progression: A Pyramid of GANs

Yang

Huang

Wang

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

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The two underlying requirements of face age progression, i.e. aging accuracy and identity permanence, are not well studied in the literature. This paper presents a novel generative adversarial network based approach to address the issues in a coupled manner. It separately models the constraints for the intrinsic subject-specific characteristics and the age-specific facial changes with respect to the elapsed time, ensuring that the generated faces present desired aging effects while simultaneously keeping personalized properties stable. To ensure photo-realistic facial details, high-level age-specific features conveyed by the synthesized face are estimated by a pyramidal adversarial discriminator at multiple scales, which simulates the aging effects with finer details. Further, an adversarial learning scheme is introduced to simultaneously train a single generator and multiple parallel discriminators, resulting in smooth continuous face aging sequences. The proposed method is applicable even in the presence of variations in pose, expression, makeup, etc., achieving remarkably vivid aging effects. Quantitative evaluations by a COTS face recognition system demonstrate that the target age distributions are accurately recovered, and 99.88% and 99.98% age progressed faces can be correctly verified at 0.001% FAR after age transformations of approximately 28 and 23 years elapsed time on the MORPH and CACD databases, respectively. Both visual and quantitative assessments show that the approach advances the state-of-the-art.

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Section: Experiments Ii: Aging Model Evaluationsupporting

confidence: 72%

Learning Continuous Face Age Progression: A Pyramid of GANs

Yang

Huang

Wang

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

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“…Models used in this work are described using two hierarchical levels, similar to those described in [7], [26]. The first level in the hierarchy, Level-1, models the changes in genuine scores, Y i,j , for each subject over time (within-subject variation), whereas, Level-2 model accounts for variation in genuine scores across different subjects (between-subject variation).…”

Section: Multilevel Statistical Modelsmentioning

confidence: 99%

“…Following the studies conducting in [7], [26], regions containing longitudinal trends for 80% 13 of the child population are plotted using estimated changes in slope and intercept parameters (σ 2 0 , σ 2 1 , σ 01 ). The regions are then used to determine the time lapse until genuine scores for 95% and 99% of the population begin to drop below thresholds at 0.01% and 0.1% FAR.…”

Section: Time Lapsementioning

confidence: 99%

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Longitudinal Study of Child Face Recognition

Deb

Nain

Jain

2018

2018 International Conference on Biometrics (ICB)

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We present a longitudinal study of face recognition performance on Children Longitudinal Face (CLF) dataset containing 3, 682 face images of 919 subjects, in the age group [2,18] years. Each subject has at least four face images acquired over a time span of up to six years. Face comparison scores are obtained from (i) a state-of-the-art COTS matcher (COTS-A), (ii) an open-source matcher (FaceNet), and (iii) a simple sum fusion of scores obtained from COTS-A and FaceNet matchers. To improve the performance of the open-source FaceNet matcher for child face recognition, we were able to fine-tune it on an independent training set of 3,294 face images of 1,119 children in the age group [3,18] years. Multilevel statistical models are fit to genuine comparison scores from the CLF dataset to determine the decrease in face recognition accuracy over time. Additionally, we analyze both the verification and open-set identification accuracies in order to evaluate state-of-the-art face recognition technology for tracing and identifying children lost at a young age as victims of child trafficking or abduction.

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“…There exists a sizable amount of literature on recognition of age-separated face images, such as [24][25][26][27][28][29][30][31][32][33][34][35][36]. The existing methods can be categorized as generative or discriminative.…”

Section: Recognition and Retrieval Of Face Images Across Aging Variatmentioning

confidence: 99%

Demographic-Assisted Age-Invariant Face Recognition and Retrieval

et al. 2018

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Demographic estimation of human face images involves estimation of age group, gender, and race, which finds many applications, such as access control, forensics, and surveillance. Demographic estimation can help in designing such algorithms which lead to better understanding of the facial aging process and face recognition. Such a study has two parts-demographic estimation and subsequent face recognition and retrieval. In this paper, first we extract facial-asymmetry-based demographic informative features to estimate the age group, gender, and race of a given face image. The demographic features are then used to recognize and retrieve face images. Comparison of the demographic estimates from a state-of-the-art algorithm and the proposed approach is also presented. Experimental results on two longitudinal face datasets, the MORPH II and FERET, show that the proposed approach can compete the existing methods to recognize face images across aging variations.

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Face Recognition Performance under Aging

Cited by 29 publications

References 18 publications

Learning Continuous Face Age Progression: A Pyramid of GANs

Learning Continuous Face Age Progression: A Pyramid of GANs

Longitudinal Study of Child Face Recognition

Demographic-Assisted Age-Invariant Face Recognition and Retrieval

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