Age estimation from facial images based on hierarchical feature selection

Bouchrika, Imed; Harrati, Nouzha; Ladjailia, Ammar; Khedairia, Soufiane

doi:10.1109/sta.2015.7505156

Cited by 10 publications

(6 citation statements)

References 19 publications

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“…In this paper [1] the author briefly explains the effect on models for anticipating the clinical states. The results gave an idea of precise and hypothesis indicators of age.…”

Section: Literature Surveymentioning

confidence: 99%

A Survey on Age Prediction Using Skin Attributes

2022

IRJMETS

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Age prediction has gained attention due to its various number of real world applications. Aging process can be usually seen from our skin texture such as it can be oily or dry. Aging can also be indicated from the facial structure such as it can be oval, round or geometric and also based on the skin color such as fair ,medium and dark . Aging progression happens with respect to the facial features.In this system we are using mobileNet to predict the age using the skin(face). It is a network model using depthwise separable convolution as its basic unit. It is a pre-trained model used for classification in which pretrained models are deep neural networks and they are trained using a large image dataset. We have proposed a Mobile Net based neural network model for age estimation in the images. The proposed approach is that we leverage the rich information contained in the DCNN model pre-trained using a large-scale face dataset for age estimation. Also, the proposed error correction module mitigates the influences of the errors made at the initial classification stage.

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“…In this paper [1] the author briefly explains the effect on models for anticipating the clinical states. The results gave an idea of precise and hypothesis indicators of age.…”

Section: Literature Surveymentioning

confidence: 99%

A Survey on Age Prediction Using Skin Attributes

2022

IRJMETS

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“…ImedBouchrika, NouzhaHarrati et al, December 2015 in their paper Age Estimation from Facial Images based on Hierarchical Feature Selection [5] proposed the use of craniofacial development theory & skin wrinkle analysis to estimate the age of a person based on their pictures. The primary and secondary features are combined to satisfactorily classify facial images into age classes.…”

Section: Related Workmentioning

confidence: 99%

An Interactive Cart with Analytics Recommendation and Tracking-iCART

R¹,

Meeradevi²,

Mundada³

et al. 2020

IJIEEB

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It is very common to use trolleys in supermarkets, they are machines which help us in easily carrying around a lot of items in the supermarket. iCart aims to extend the services offered by these trolleys by augmenting features such as indoor navigation, product recommendation and instantaneous reply to customer queries. For indoor navigation the RSSI values of the bluetooth modules are used to find the customers coordinates and dijkstra's algorithm is used for finding the shortest routes, for product recommendation age, gender and month of the year are passed as input parameters to a classification model and for replying to customer queries a chatbot is implemented using RASA framework. All the features mentioned will be integrated in a single LCD screen mounted on the trolley. This system not only wanes the energy spent by the customer foraging for items, but also increases the owner's profits by providing product recommendations. This model is been implemented using IoT and Machine Learning techniques to save time of customer.

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“…Age estimation is a very prolific area of research within the computer vision community (Huerta et al, 2015 ; Drobnyh and Polovinkin, 2017 ). 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 ).…”

Section: Introductionmentioning

confidence: 99%

“…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).…”

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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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Age estimation from facial images based on hierarchical feature selection

Cited by 10 publications

References 19 publications

A Survey on Age Prediction Using Skin Attributes

A Survey on Age Prediction Using Skin Attributes

An Interactive Cart with Analytics Recommendation and Tracking-iCART

Apparent age prediction from faces: A survey of modern approaches

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