Apparent age prediction from faces: A survey of modern approaches

Agbo-Ajala, Olatunbosun; Viriri, Serestina; Oloko-Oba, Mustapha; Ekundayo, Olufisayo; Heymann, Reolyn

doi:10.3389/fdata.2022.1025806

Cited by 7 publications

(4 citation statements)

References 48 publications

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“…In the brain and face dataset, performance was poorer than just the face alone and greater than when defacing was conducted (MAE: 7.06). Much prior work has aimed to predict a person's chronological age from 2-dimensional photos using CNNs [17] although we do not know of prior work predicting age from facial regions of brain MRI.…”

Section: Discussionmentioning

confidence: 99%

The Influence of Brain MRI Defacing Algorithms on Brain-Age Predictions via 3D Convolutional Neural Networks

Cali

Bhatt

Thomopoulos

et al. 2023

Preprint

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In brain imaging research, it is becoming standard practice to remove the face from the individual′s 3D structural MRI scan to ensure data privacy standards are met. Face removal – or ′defacing′ – is being advocated for large, multi– site studies where data is transferred across geographically diverse sites. Several methods have been developed to limit the loss of important brain data by accurately and precisely removing non-brain facial tissue. At the same time, deep learning methods such as convolutional neural networks (CNNs) are increasingly being used in medical imaging research for diagnostic classification and prognosis in neurological diseases. These neural networks train predictive models based on patterns in large numbers of images. Because of this, defacing scans could remove informative data. Here, we evaluated 4 popular defacing methods to identify the effects of defacing on ′ brain age′ prediction – a common benchmarking task of predicting a subject′s chronological age from their 3D T1– weighted brain MRI. We compared brain– age calculations using defaced MRIs to those that were directly brain extracted, and those with both brain and face. Significant differences were present when comparing average per– subject error rates between algorithms in both the defaced brain data and the extracted facial tissue. Results also indicated brain age accuracy depends on defacing and the choice of algorithm. In a secondary analysis, we also examined how well comparable CNNs could predict chronological age from the facial region only (the extracted portion of the defaced image), as well as visualize areas of importance in facial tissue for predictive tasks using CNNs. We obtained better performance in age prediction when using the extracted face portion alone than images of the brain, suggesting the need for caution when defacing methods are used in medical image analysis.

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Section: Discussionmentioning

confidence: 99%

The Influence of Brain MRI Defacing Algorithms on Brain-Age Predictions via 3D Convolutional Neural Networks

Cali

Bhatt

Thomopoulos

et al. 2023

Preprint

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“…There are several methods for gender classification, and age estimation, in this section we summarized the most public methods for gender classification as shown in Figure 3, where the prospects and contradictions for these methods are tabulated in Table I. Similarly, the age estimation methods are summarized in Figure 4, and the prospects and contradictions are tabulated in Table II [46]. Hence, it represents an arithmetic average of the absolute errors ei, in which yi denotes the estimated age of ith image and xi expresses the true age, and n refers to the image's number.…”

Section: General Methods Used For Gender and Age Estimationmentioning

confidence: 99%

Gender and Age Estimation from Human Faces Based on Deep Learning Techniques: A Review

Ghrban¹,

Abbadi²

2023

IJCDS

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Due to the advancement of the methodologies employed in this field and the increased attention being paid to the deep learning (DL) techniques' implementation, focusing on convolutional neural networks (CNNs), gender and age estimates have recently assumed a significant amount of relevance. It is important to precisely predict the gender, including the age of a person, provided that it is used in many applications for smart devices, including those related to security, health, and other areas. Although there have been several studies and research in this area, gender, and age estimation still confront certain problems and difficulties, such as existing of earrings, races, masked faces, makeup, etc. which might interfere with the systems' operations and decrease their accuracy. In this paper, we assess the accuracy of the models employed in three of the most well-known datasets: MORPH2, FG-NET, and OUI-Adience. Our focus is on the best and most recent technology available in this field. Additionally, we have mentioned a list of most of the challenges that may face in the process of estimating age and gender, as well as a list of applications and areas in which it can be used.

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“…Age prediction can be also reduced to a multi-class classification problem where a person's age is classified to a specific age group (i.e., within range (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)). Herein, age estimation is considered a more complex task than gender estimation since the target age groups can be more than those of the gender counterpart.…”

Section: Demographic Estimation (Age and Gender Prediction)mentioning

confidence: 99%

“…Another variation of the first component of demographic cue recognition is apparent age estimation [26] which differentiates from real age inference in terms of assuming ground truth data which derive from the annotator's estimation when the actual age of each consumer is unknown. In this respect, we can distinguish two main families of approaches.…”

Section: Demographic Estimation (Age and Gender Prediction)mentioning

confidence: 99%

A New Benchmark for Consumer Visual Tracking and Apparent Demographic Estimation from RGB and Thermal Images

Panagos,

Giotis,

Sofianopoulos

et al. 2023

Sensors

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Visual tracking and attribute estimation related to age or gender information of multiple person entities in a scene are mature research topics with the advent of deep learning techniques. However, when it comes to indoor images such as video sequences of retail consumers, data are not always adequate or accurate enough to essentially train effective models for consumer detection and tracking under various adverse factors. This in turn affects the quality of recognizing age or gender for those detected instances. In this work, we introduce two novel datasets: Consumers comprises 145 video sequences compliant to personal information regulations as far as facial images are concerned and BID is a set of cropped body images from each sequence that can be used for numerous computer vision tasks. We also propose an end-to-end framework which comprises CNNs as object detectors, LSTMs for motion forecasting of the tracklet association component in a sequence, along with a multi-attribute classification model for apparent demographic estimation of the detected outputs, aiming to capture useful metadata of consumer product preferences. Obtained results on tracking and age/gender prediction are promising with respect to reference systems while they indicate the proposed model’s potential for practical consumer metadata extraction.

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Apparent age prediction from faces: A survey of modern approaches

Cited by 7 publications

References 48 publications

The Influence of Brain MRI Defacing Algorithms on Brain-Age Predictions via 3D Convolutional Neural Networks

The Influence of Brain MRI Defacing Algorithms on Brain-Age Predictions via 3D Convolutional Neural Networks

Gender and Age Estimation from Human Faces Based on Deep Learning Techniques: A Review

A New Benchmark for Consumer Visual Tracking and Apparent Demographic Estimation from RGB and Thermal Images

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