Longitudinal Study of Automatic Face Recognition

Best-Rowden, Lacey; Jain, Anil K.

doi:10.1109/tpami.2017.2652466

Cited by 103 publications

(49 citation statements)

References 25 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%

“…Five-fold cross validation is conducted to simulate aged faces. On CACD, each fold contains 400 individuals with nearly 10,079, 8,635, 7,964, and 6,011 face images from the four age clusters of [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], [31][32][33][34][35][36][37][38][39][40], [41][42][43][44][45][46][47][48][49][50], and [51-60] years, respectively; Fig. 7: Aging effects obtained on the CACD databases for 24 different subjects.…”

Section: Experiments I: Aging Effect Simulationmentioning

confidence: 99%

See 1 more Smart Citation

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%

Section: Experiments I: Aging Effect Simulationmentioning

confidence: 99%

Learning Continuous Face Age Progression: A Pyramid of GANs

Yang

Huang

Wang

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

Self Cite

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show abstract

“…One can observe that aging features have the most significant impact on both the identification accuracy and mAP compared to the race and gender features for MORPH II and FERET datasets. This is because aging features are heterogeneous in nature, i.e., more person-specific [36] thus helpful in recognizing face images across aging. In contrast, race and gender features are more population-specific and less person specific, as suggested in [3,4].…”

Section: Results Related Discussionmentioning

confidence: 99%

“…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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“…A local feature-based multiview discriminative learning (MDL) approach is presented in (20). More recently, the effects of facial aging on recognition performance across a large population have been studied in (21). Despite the state-of-the-art performance, these approaches exhibit two main limitations: (i) lack of facial feature evaluations with temporal variations and (ii) a lack of discriminative information because most of these methods utilize a single set of facial features.…”

Section: Recognition Of Age-separated Face Imagesmentioning

confidence: 99%

Facial Asymmetry‐Based Age Group Estimation: Role in Recognizing Age‐Separated Face Images

Sajid

Taj

Bajwa

et al. 2018

Journal of Forensic Sciences

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Face recognition aims to establish the identity of a person based on facial characteristics. On the other hand, age group estimation is the automatic calculation of an individual's age range based on facial features. Recognizing age-separated face images is still a challenging research problem due to complex aging processes involving different types of facial tissues, skin, fat, muscles, and bones. Certain holistic and local facial features are used to recognize age-separated face images. However, most of the existing methods recognize face images without incorporating the knowledge learned from age group estimation. In this paper, we propose an age-assisted face recognition approach to handle aging variations. Inspired by the observation that facial asymmetry is an age-dependent intrinsic facial feature, we first use asymmetric facial dimensions to estimate the age group of a given face image. Deeply learned asymmetric facial features are then extracted for face recognition using a deep convolutional neural network (dCNN). Finally, we integrate the knowledge learned from the age group estimation into the face recognition algorithm using the same dCNN. This integration results in a significant improvement in the overall performance compared to using the face recognition algorithm alone. The experimental results on two large facial aging datasets, the MORPH and FERET sets, show that the proposed age group estimation based on the face recognition approach yields superior performance compared to some existing state-of-the-art methods.

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Longitudinal Study of Automatic Face Recognition

Cited by 103 publications

References 25 publications

Learning Continuous Face Age Progression: A Pyramid of GANs

Learning Continuous Face Age Progression: A Pyramid of GANs

Demographic-Assisted Age-Invariant Face Recognition and Retrieval

Facial Asymmetry‐Based Age Group Estimation: Role in Recognizing Age‐Separated Face Images

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