Improving Borderline Adulthood Facial Age Estimation through Ensemble Learning

Anda, Felix; Lillis, David; Kanta, Aikaterini; Becker, Brett A.; Bou‐Harb, Elias; Le-Khac, Nhien-An; Scanlon, Mark

doi:10.1145/3339252.3341491

Cited by 11 publications

(7 citation statements)

References 25 publications

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“…This range of ages was selected because it presents the lowest frequencies in the considered facial datasets. In addition, young faces are crucial in cybersecurity applications, such as access control, the detection of Child Sexual Exploitation Material or the identification of victims of child abuse [49][50][51]. Young facial images are considered to be of medium variability content since faces have a similar shape structure; the StyleGAN model was originally designed for faces generation.…”

Section: Input Target Domain Imagesmentioning

confidence: 99%

StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications

2021

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Deep learning applications on computer vision involve the use of large-volume and representative data to obtain state-of-the-art results due to the massive number of parameters to optimise in deep models. However, data are limited with asymmetric distributions in industrial applications due to rare cases, legal restrictions, and high image-acquisition costs. Data augmentation based on deep learning generative adversarial networks, such as StyleGAN, has arisen as a way to create training data with symmetric distributions that may improve the generalisation capability of built models. StyleGAN generates highly realistic images in a variety of domains as a data augmentation strategy but requires a large amount of data to build image generators. Thus, transfer learning in conjunction with generative models are used to build models with small datasets. However, there are no reports on the impact of pre-trained generative models, using transfer learning. In this paper, we evaluate a StyleGAN generative model with transfer learning on different application domains—training with paintings, portraits, Pokémon, bedrooms, and cats—to generate target images with different levels of content variability: bean seeds (low variability), faces of subjects between 5 and 19 years old (medium variability), and charcoal (high variability). We used the first version of StyleGAN due to the large number of publicly available pre-trained models. The Fréchet Inception Distance was used for evaluating the quality of synthetic images. We found that StyleGAN with transfer learning produced good quality images, being an alternative for generating realistic synthetic images in the evaluated domains.

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Section: Input Target Domain Imagesmentioning

confidence: 99%

StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications

2021

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“…Due to the advancement of deep learning architectures, the performance of age estimators has improved significantly in recent years [7], [5], [8]. Despite this, to our knowledge, there are very few approaches that estimate the age of minor/young adults [9] or eye-occluded facial images [10].…”

Section: Related Workmentioning

confidence: 99%

Reinforcement of age estimation in forensic tools to detect Child Sexual Exploitation Material

Biswas¹,

Chaves²,

Jáñez-Martino³

et al. 2021

Colección Jornadas Y Congresos

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“…Forensic laboratories very often examine digital evidence during a criminal investigation. In particular, the criminal investigation of Child Sexual Exploitation Material (CSEM) shows a growing interest internationally [1]. Advances in technology have increased the use of mobile devices, social media and P2P networks, making it easier for offenders to create and distribute CSEM, something that has become highly prevalent worldwide.…”

Section: Introductionmentioning

confidence: 99%

Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

Chaves

Fidalgo

Alegre

et al. 2020

Sensors

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Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require large computation power and processing time. In this work, we evaluate the speed–accuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy. We expect this to become a very useful tool for the end user in forensic laboratories in order to estimate the performance for different face detection options. Experimental results showed that the best speed–accuracy tradeoff is achieved with images resized to 50% of the original size in GPUs and images resized to 25% of the original size in CPUs. Moreover, performance can be estimated using multiple linear regression models with a Mean Absolute Error (MAE) of 0.113, which is very promising for the forensic field.

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Improving Borderline Adulthood Facial Age Estimation through Ensemble Learning

Cited by 11 publications

References 25 publications

StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications

StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications

Reinforcement of age estimation in forensic tools to detect Child Sexual Exploitation Material

Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

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