A Review on the Suitability of Machine Learning Approaches to Facial Age Estimation

Onifade, Olufade F. W.; Akinyemi, Joseph Damilola

doi:10.5815/ijmecs.2015.12.03

Cited by 5 publications

(3 citation statements)

References 49 publications

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“…The images captured were run through MATLAB for the purpose of training and testing. In this, 1-D Gray Scale histogram equalization was adopted for image enhancement [18] [19] while 3D Gabor filter was used for feature extraction [20] and K-NN classifier algorithm was as well adopted for classification.…”

Section: Methodsmentioning

confidence: 99%

A Soft Computing Model of Soft Biometric Traits for Gender and Ethnicity Classification

Aworinde¹,

O.F.W.²

2019

IJEM

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There is paucity of information on the possibility of ethnicity identification through fingerprint biometric characteristics and so, this work is set to combine two soft biometric traits (Gender and Ethnicity) in order to ascertain if individual of different ethnicity and gender bias can be identified through their fingerprint. Live scan mechanism was used in order to minimize human errors and as well speed up the rate of fingerprint acquisition which unequivocally ensure good quality capturing of the fingerprint image. In this work, fingerprints of over a thousand people from three different ethnic groups of both male and female gender in Nigeria were captured and subjected to training, testing and classification using Gabor filter and K-NN respectively. Histogram equalization was used for image enhancement and the system performance was evaluated on the basis of some selected metrics such as Recognition Accuracy, Average Recognition Time, Specificity and Sensitivity. Result of this work indicated over 96% accuracy in predicting person's ethnicity and gender with an average recognition time of less than 2secs.

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Section: Methodsmentioning

confidence: 99%

A Soft Computing Model of Soft Biometric Traits for Gender and Ethnicity Classification

Aworinde¹,

O.F.W.²

2019

IJEM

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“…There has been an increasing interest in age estimation from facial images (Drobnyh and Polovinkin, 2017 ) due to its increasing demands in various potential applications in security control (Abbas and Kareem, 2018 ), human-computer interaction (Abbas and Kareem, 2018 ), social media (Ruiz-Del-Solar et al, 2009 ), and forensic studies (Bouchrika et al, 2016 ). Although this subject has been extensively studied, the ability to estimate human ages reliably and correctly from face images is still far from satisfying human performance level (Onifade, 2015 ). There exist two kinds of facial age estimation: One is real (biological) age estimation, which determines the precise chronological or biological age of a person from the facial image (Shen et al, 2018 ); the other is apparent age estimation (Agustsson et al, 2017 ), which focuses on “how old does a person looks like” rather than predicting the real or biological age.…”

Section: Introductionmentioning

confidence: 99%

Apparent age prediction from faces: A survey of modern approaches

Agbo-Ajala

Viriri²,

Oloko-Oba³

et al. 2022

Front. Big Data

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Apparent age estimation via human face image has attracted increased attention due to its numerous real-world applications. Predicting the apparent age has been quite difficult for machines and humans. However, researchers have focused on machine estimation of “age as perceived” to a high level of accuracy. To further improve the performance of apparent age estimation from the facial image, researchers continue to examine different methods to enhance its results further. This paper presents a critical review of the modern approaches and techniques for the apparent age estimation task. We also present a comparative analysis of the performance of some of those approaches on the apparent facial aging benchmark. The study also highlights the strengths and weaknesses of each approach used for apparent age estimation to guide in choosing the appropriate algorithms for future work in the field. The work focuses on the most popular algorithms and those that appear to have been the most successful for apparent age estimation to improve on the existing state-of-the-art results. We based our evaluations on three facial aging datasets, including looking at people (LAP)-2015, LAP-2016, and APPA-REAL, the most popular and publicly available datasets benchmark for apparent age estimation.

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“…There has been an increasing interest toward age estimation from facial images [1] due to its increasing demands in various potential applications including security control [3], human-computer interaction [3], social media [4] and forensic studies [5] [6]. Although this subject has been extensively studied, the ability to estimate human ages reliably and correctly from face images is still far from satisfying human performance levels [7]. There exist two kinds of facial age estimation.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Convolutional Neural Network for Real and Apparent Age Estimation in Unconstrained Face Images

Agbo-Ajala

Viriri

2020

IEEE Access

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Real and apparent age estimation of human face has attracted increased attention due to its numerous real-world applications. Different intelligent application scenarios can benefit from these computer-based systems that predict the ages of people correctly. Automatic apparent age system is particularly useful in medical diagnosis, facial beauty product development, movie role casting, the effect of plastic surgery, and anti-aging treatment. Predicting the real and apparent age of people has been quite difficult for both machines and humans. More recently, Deep learning with Convolutional Neural Networks (CNNs) methods have been extensively used for these classification task. It has incomparable advantages in extracting discriminative image features from human faces. However, many of the existing CNN-based methods are designed to be deeper and larger with more complex layers that makes it challenging to deploy on mobile devices with resource-constrained features. Therefore, we design a lightweight CNN model of fewer layers to estimate the real and apparent age of individuals from unconstrained real-time face images that can be deployed on mobile devices. The experimental results, when analyzed for classification accuracy on FG-NET, MORPH-II and APPA-REAL, with large-scale face images containing both real and apparent age annotations, show that our model obtains a state-of-the-art performance in both real and apparent age classification when compared to state-of-the-art methods. The new results and model size, therefore, confirm the usefulness of the model on resource-constrained mobile devices.INDEX TERMS apparent age, unconstrained images, convolutional neural network, mobile devices.

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A Review on the Suitability of Machine Learning Approaches to Facial Age Estimation

Cited by 5 publications

References 49 publications

A Soft Computing Model of Soft Biometric Traits for Gender and Ethnicity Classification

A Soft Computing Model of Soft Biometric Traits for Gender and Ethnicity Classification

Apparent age prediction from faces: A survey of modern approaches

A Lightweight Convolutional Neural Network for Real and Apparent Age Estimation in Unconstrained Face Images

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