Age Estimation by Super-Resolution Reconstruction Based on Adversarial Networks

A high-resolution thermal camera is very expensive and is thus difficult to be used. Furthermore, thermal images become blurred in various cases of object motion, camera shaking, and camera defocusing. To solve these problems, a previous super-resolution restoration (SRR) technique converting a thermal image acquired by a low-resolution camera into a highresolution one, and a thermal image deblurring method have been researched. However, existing studies were performed based on 1-channel (grayscale) images. In addition, a large-sized and whole image has been used in the existing thermal image deblurring methods, which causes lower deblurring performance. In this study, we propose novel SRR and deblurring methods. The proposed deblurring method is conducted based on small region images. The proposed methods are also conducted using 3-channel (color) thermal images and generative adversarial networks. In addition, the performances of this method are compared in various color spaces (RGB, Gray, HLS, HSV, Lab, Luv, XYZ, YCrCb), image sizes, and thermal databases. Through experiments using selfcollected databases and open databases, it was confirmed that the proposed methods show better performance than the state-ofthe-art methods. INDEX TERMS thermal image; super-resolution reconstruction; deep learning; generative adversarial network; image deblurring.

show abstract

“…Entire image [42][43][44][45][46][47][48][49][50][51][52] The method extracts suitable features in various camera settings and environments.…”

Section: With Deeplearning Methodsmentioning

confidence: 99%

“…Ref. [47] proposed age estimation using CNN and SRR methods with a GAN. In addition, GAN-based [48,49] methods were developed to obtain better results than those obtained with CNN-based methods.…”

Section: B Image Deblurringmentioning

confidence: 99%

Thermal Image Reconstruction Using Deep Learning

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Age estimation is a very important task across many domains such as advertising, human–computer interaction and security. There are many works [ 14 , 15 , 23 , 24 , 43 , 53 , 72 , 75 , 82 ] addressing the problem of age estimation of people from facial images. Before the era of deep learning, the age estimation task was tackled by extracting handcrafted features from facial images, then applying a regressor or a classifier on top of the extracted features.…”

Section: Related Workmentioning

confidence: 99%

Teacher–student training and triplet loss to reduce the effect of drastic face occlusion

Duta

Ionescu

2021

Machine Vision and Applications

View full text Add to dashboard Cite

We study a series of recognition tasks in two realistic scenarios requiring the analysis of faces under strong occlusion. On the one hand, we aim to recognize facial expressions of people wearing virtual reality headsets. On the other hand, we aim to estimate the age and identify the gender of people wearing surgical masks. For all these tasks, the common ground is that half of the face is occluded. In this challenging setting, we show that convolutional neural networks trained on fully visible faces exhibit very low performance levels. While fine-tuning the deep learning models on occluded faces is extremely useful, we show that additional performance gains can be obtained by distilling knowledge from models trained on fully visible faces. To this end, we study two knowledge distillation methods, one based on teacher–student training and one based on triplet loss. Our main contribution consists in a novel approach for knowledge distillation based on triplet loss, which generalizes across models and tasks. Furthermore, we consider combining distilled models learned through conventional teacher–student training or through our novel teacher–student training based on triplet loss. We provide empirical evidence showing that, in most cases, both individual and combined knowledge distillation methods bring statistically significant performance improvements. We conduct experiments with three different neural models (VGG-f, VGG-face and ResNet-50) on various tasks (facial expression recognition, gender recognition, age estimation), showing consistent improvements regardless of the model or task.

show abstract

“…The author in [30] took cognizance of images taken in an uncontrolled environment and real-life situations using mobile phones. Such images have a degraded quality due to the phone camera's resolution.…”

Section: Related Workmentioning

confidence: 99%

Face Age Estimation and the Other-race Effect

Oladipo¹,

Omidiora²,

Osamor³

2021

IJACSA

View full text Add to dashboard Cite

Age estimation is an automated method of predicting human age from 2-D facial feature representations. The majority of studies carried out in this research area use the FG-NET and MORPH 2 databases to train and test developed systems, which are lacking in black-face content. Most age frauds are perpetuated in the sub-Saharan African region due to the unavailability of an official database and unregistered births in the rural areas. The issues of unverified age in the region made it possible for under-age voters, under-age drivers, and the engagement of over-aged sportsmen. The other-race effect could reduce the performance of face recognition techniques, which could make techniques that work for white faces underperform when deployed for use in the predominately black face region. This study examines the other-race effect on face-based age estimation by analyzing the accuracy of an age estimation system trained with predominantly black faces against the same age estimation system trained with predominately white faces. The developed age estimation system uses a genetic algorithmartificial neural network classifier and local binary pattern for texture and shape feature extraction. A total of 170 black faces were used for system testing. The result showed that the age estimation system trained with the predominantly black face database (GA-ANN-AES-855) outperformed the system trained with predominantly white faces (GA-ANN-AES-255) on testing with the aforementioned black face samples. The results obtained from the simulation were further subjected to inferential statistics, which established that the improvement in the correct classification rate was statistically significant. Hence, the otherrace effect affects face-based age estimation systems.

show abstract

Age Estimation by Super-Resolution Reconstruction Based on Adversarial Networks

Cited by 26 publications

References 67 publications

Thermal Image Reconstruction Using Deep Learning

Thermal Image Reconstruction Using Deep Learning

Teacher–student training and triplet loss to reduce the effect of drastic face occlusion

Face Age Estimation and the Other-race Effect

Contact Info

Product

Resources

About