Deep Face Age Progression: A Survey

Grimmer, Marcel; Ramachandra, Raghavendra; Busch, Christoph

doi:10.1109/access.2021.3085835

Cited by 16 publications

(4 citation statements)

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“…GAN-based architectures have not only proven their worth in generating synthetic images but also in performing face-age progression (FAP). Grimmer et al (2021) have recently provided a comprehensive survey of deep face age progression, noting that GANs indeed produce remarkable face ageing results (cf. Figure 1).…”

Section: Face-age Progressionmentioning

confidence: 99%

Child face recognition at scale: synthetic data generation and performance benchmark

Falkenberg,

Bensen Ottsen,

Ibsen

et al. 2024

Front. Signal Process.

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We address the need for a large-scale database of children’s faces by using generative adversarial networks (GANs) and face-age progression (FAP) models to synthesize a realistic dataset referred to as “HDA-SynChildFaces”. Hence, we proposed a processing pipeline that initially utilizes StyleGAN3 to sample adult subjects, which is subsequently progressed to children of varying ages using InterFaceGAN. Intra-subject variations, such as facial expression and pose, are created by further manipulating the subjects in their latent space. Additionally, this pipeline allows the even distribution of the races of subjects, allowing the generation of a balanced and fair dataset with respect to race distribution. The resulting HDA-SynChildFaces consists of 1,652 subjects and 188,328 images, each subject being present at various ages and with many different intra-subject variations. We then evaluated the performance of various facial recognition systems on the generated database and compared the results of adults and children at different ages. The study reveals that children consistently perform worse than adults on all tested systems and that the degradation in performance is proportional to age. Additionally, our study uncovers some biases in the recognition systems, with Asian and black subjects and females performing worse than white and Latino-Hispanic subjects and males.

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Section: Face-age Progressionmentioning

confidence: 99%

Child face recognition at scale: synthetic data generation and performance benchmark

Falkenberg,

Bensen Ottsen,

Ibsen

et al. 2024

Front. Signal Process.

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“…In this section, we introduce the existing face aging methods that are more relevant to our approach, while a comprehensive review of face aging works can be found in [12], [13].…”

Section: Related Workmentioning

confidence: 99%

Re-Aging GAN++: Temporally Consistent Transformation of Faces in Videos

Makhmudkhujaev,

Hong,

Park

2023

IEEE Access

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The challenge of transforming the apparent age of human faces in videos has not been adequately addressed due to the complexities involved in preserving spatial and temporal consistency. This task is further complicated by the scarcity of video datasets featuring specific individuals across various age groups. To address these issues, we introduce Re-Aging GAN++ (RAGAN++), a unified framework designed to perform facial age transformation in videos utilizing an innovative GAN-based model trained on still image data. Initially, the modulation process acquires multi-scale personalized age features to depict the attributes of the target age group. Subsequently, the encoder applies Gaussian smoothing at each scale, ensuring a seamless frame-to-frame transition that accounts for inter-frame variations, such as facial motion within the camera's field of view. Remarkably, the proposed model demonstrates the ability to perform facial age transformation in videos despite being trained exclusively on image data. Our proposed method exhibits exceptional spatio-temporal consistency concerning facial identity, expression, and pose while maintaining natural variations across diverse age groups.INDEX TERMS Video generation, age manipulation, GAN, spatio-temporal consistency.

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“…Thereafter, the resulting latent code can be shifted in the latent space, whereby the inverted image of the shifted vector results in an alteration of the original image. The technique can, for instance, be used for face age progression [13]. In addition to the face, some research have also been conducted for other biometric modalities, e.g.…”

Section: A Synthetic Data Generation For Face Analysismentioning

confidence: 99%

Face Beneath the Ink: Synthetic Data and Tattoo Removal with Application to Face Recognition

Ibsen¹,

Rathgeb²,

Drozdowski³

et al. 2022

Preprint

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Systems that analyse faces have seen significant improvements in recent years and are today used in numerous application scenarios. However, these systems have been found to be negatively affected by facial alterations such as tattoos. To better understand and mitigate the effect of facial tattoos in facial analysis systems, large datasets of images of individuals with and without tattoos are needed. To this end, we propose a generator for automatically adding realistic tattoos to facial images. Moreover, we demonstrate the feasibility of the generation by training a deep learning-based model for removing tattoos from face images. The experimental results show that it is possible to remove facial tattoos from real images without degrading the quality of the image. Additionally, we show that it is possible to improve face recognition accuracy by using the proposed deep learning-based tattoo removal before extracting and comparing facial features.

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Deep Face Age Progression: A Survey

Cited by 16 publications

References 100 publications

Child face recognition at scale: synthetic data generation and performance benchmark

Child face recognition at scale: synthetic data generation and performance benchmark

Re-Aging GAN++: Temporally Consistent Transformation of Faces in Videos

Face Beneath the Ink: Synthetic Data and Tattoo Removal with Application to Face Recognition

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