Computational Intelligence in Automatic Face Age Estimation: A Survey

Osman, Omaima FathElrahman; Yap, Moi Hoon

doi:10.1109/tetci.2018.2864554

Cited by 19 publications

(10 citation statements)

References 102 publications

(184 reference statements)

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“…Extraction of ageing patterns (feature extraction) from facial images manually, using a set of rules and algorithms is the first phase in AFAE involving handcrafted traditional machine learning based methods. These features have been categorized into: local, global and hybrid [21]. Anthropometric based models [17], Active Appearance Model (AAM) [6] as well as Bio-Inspired Features (BIF) created by Gabor filters [12] and Local Binary Patterns (LBP) [36] were also vastly used to learn texture and shape features from the training facial images.…”

Section: Related Workmentioning

confidence: 99%

Cross-Racial Automatic Age Estimation from Facial Images using Deep Learning

2021

IJETER

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This paper presents a deep learning approach for age estimation of human beings using their facial images. The different racial groups based on skin colour have been incorporated in the annotations of the images in the dataset, while ensuring an adequate distribution of subjects across the racial groups so as to achieve an accurate Automatic Facial Age Estimation (AFAE). The principle of transfer learning is applied to the ResNet50 Convolutional Neural Network (CNN) initially pretrained for the task of object classification and finetuning it’s hyperparameters to propose an AFAE system that can be used to automate ages of humans across multiple racial groups. The mean absolute error of 4.25 years is obtained at the end of the research which proved the effectiveness and superiority of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Cross-Racial Automatic Age Estimation from Facial Images using Deep Learning

2021

IJETER

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“…In one of the earliest face age model studies, Kwon and Lobo [18] classified age into three categories: infant, adult, and senior, and used simple feature extraction and machine learning for face age classification. Subsequently, computer science and pattern recognition researchers introduced various age classification/estimation methods [19,20]. Earlier machine learning methods typically included one (or more) feature extractor and one (or more) age classifier (or estimator).…”

Section: Face Age Database Estimation Modelmentioning

confidence: 99%

A Survey on Deep Learning Face Age Estimation Model: Method and Ethnicity

Dahlan¹

2021

IJACSA

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Face age estimation is a type of study in computer vision and pattern recognition. Designing an age estimation or classification model requires data as training samples for the machine to learn. Deep learning method has improved estimation accuracy and the number of deep learning age estimation models developed. Furthermore, numerous datasets availability is making the method an increasingly attractive approach. However, face age databases mostly have limited ethnic subjects, only one or two ethnicities and may result in ethnic bias during age estimation, thus impeding progress in understanding face age estimation. This paper reviewed available face age databases, deep learning age estimation models, and discussed issues related to ethnicity when estimating age. The review revealed changes in deep learning architectural designs from 2015 to 2020, frequently used face databases, and the number of different ethnicities considered. Although model performance has improved, the widespread use of specific few multi-races databases, such as the MORPH and FG-NET databases, suggests that most age estimation studies are biased against non-Caucasians/non-white subjects. Two primary reasons for face age research's failure to further discover and understand ethnic traits effects on a person's facial aging process: lack of multi-race databases and ethnic traits exclusion. Additionally, this study presented a framework for accounting ethnic in face age estimation research and several suggestions on collecting and expanding multi-race databases. The given framework and suggestions are also applicable for other secondary factors (e.g. gender) that affect face age progression and may help further improve future face age estimation research.

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“…As classification techniques classify age into multiple age groups such as babies, young people, middle-aged, and older adult, performance is evaluated using classification accuracy [12]. On the other hand, regression techniques predict ages and thus are evaluated by the mean absolute error (MAE) between ground-truth age and predicted age.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, regression techniques predict ages and thus are evaluated by the mean absolute error (MAE) between ground-truth age and predicted age. Table 1 presents the age learning techniques, feature extraction techniques, and databases of existing age estimation studies [12]. The study by [1] used a deep learning technique to solve the age estimation problem.…”

Section: Related Workmentioning

confidence: 99%

“…Feature extraction converts the changes in the appearance of one's face during the aging process to the features used for age estimation [11]. Features thus extracted can be classified as local features or global features [12]. Local features are extracted from the forehead, eye rims, cheeks, and other parts of the face that clearly display age-related characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Age Estimation by Super-Resolution Reconstruction Based on Adversarial Networks

et al. 2020

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Age estimation using facial images is applicable in various fields, such as age-targeted marketing, analysis of demand and preference for goods, skin care, remote medical service, and age statistics, for describing a specific place. However, if a low-resolution camera is used to capture the images, or facial images are obtained from the subjects standing afar, the resolution of the images is degraded. In such a case, information regarding wrinkles and the texture of the face are lost, and features that are crucial for age estimation cannot be obtained. Existing studies on age estimation did not consider the degradation of resolution but used only high-resolution facial images. To overcome this limitation, this paper proposes a deep convolutional neural network (CNN)-based age estimation method that reconstructs low-resolution facial images as high-resolution images using a conditional generative adversarial network (GAN), and then uses the images as inputs. An experiment is conducted using two open databases (PAL and MORPH databases). The results demonstrate that the proposed method achieves higher accuracy in high-resolution reconstruction and age estimation than the state-of-the art methods.

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Computational Intelligence in Automatic Face Age Estimation: A Survey

Cited by 19 publications

References 102 publications

Cross-Racial Automatic Age Estimation from Facial Images using Deep Learning

Cross-Racial Automatic Age Estimation from Facial Images using Deep Learning

A Survey on Deep Learning Face Age Estimation Model: Method and Ethnicity

Age Estimation by Super-Resolution Reconstruction Based on Adversarial Networks

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