Landmark-Guided Local Deep Neural Networks for Age and Gender Classification

Zhang, Yungang; Xu, Tao

doi:10.1155/2018/5034684

Cited by 12 publications

(3 citation statements)

References 30 publications

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“…Lately, deep learning is being used in many computer vision applications [61][62][63][64]. Therefore, studies have proposed varying architectures and experimental setups for CNNs to improve gender recognition [5,14,16,21,23,24,30,40,44,49,62,[65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80]. Other authors have used ensemble learning [58] and K-nearest neighbor (KNN) [63] methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Face Gender Recognition in the Wild: An Extensive Performance Comparison of Deep-Learned, Hand-Crafted, and Fused Features with Deep and Traditional Models

et al. 2020

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Face gender recognition has many useful applications in human–robot interactions as it can improve the overall user experience. Support vector machines (SVM) and convolutional neural networks (CNNs) have been used successfully in this domain. Researchers have shown an increased interest in comparing and combining different feature extraction paradigms, including deep-learned features, hand-crafted features, and the fusion of both features. Related research in face gender recognition has been mostly restricted to limited comparisons of the deep-learned and fused features with the CNN model or only deep-learned features with the CNN and SVM models. In this work, we perform a comprehensive comparative study to analyze the classification performance of two widely used learning models (i.e., CNN and SVM), when they are combined with seven features that include hand-crafted, deep-learned, and fused features. The experiments were performed using two challenging unconstrained datasets, namely, Adience and Labeled Faces in the Wild. Further, we used T-tests to assess the statistical significance of the differences in performances with respect to the accuracy, f-score, and area under the curve. Our results proved that SVMs showed best performance with fused features, whereas CNN showed the best performance with deep-learned features. CNN outperformed SVM significantly at p < 0.05.

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Section: Literature Reviewmentioning

confidence: 99%

Face Gender Recognition in the Wild: An Extensive Performance Comparison of Deep-Learned, Hand-Crafted, and Fused Features with Deep and Traditional Models

et al. 2020

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“…Using the (LFW) dataset and Adience dataset, another local deep neural network was introduced in [16]. Accuracy of 96.02% and 80.64% for gender prediction was obtained from these two datasets and accuracy of 44.36% for age prediction was achieved from the proposed model.…”

Section: Literature Reviewmentioning

confidence: 99%

A Lightweight Deep Convolutional Neural Network Model for Real-Time Age and Gender Prediction

Opu

Koly

Das

et al. 2020

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)

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Recognition of age and gender has become a significant part of the biometric system, protection, and treatment. It is widely used for people to access age-related content. It is used by social media in the distribution of layered advertising and promotions to expand its scope. Application of face detection has grown to a great extent that we should upgrade it using various methods to achieve more accurate results. In this paper, we have developed a lightweight deep Convolution neural network model for real-time age and gender prediction. For making the training dataset more diverse, Wiki, UTKFace, and Adience datasets have been merged into one containing 18728 images. Using this vast mixed dataset, we have achieved accuracy of 48.59% and 80.76% for age and gender respectively. Further, the model is tested in real-time. Different experimental investigations on the prepared dataset show that with most recent approaches, our model provides competitive prediction accuracy.

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“…Each of these face analysis tasks (age and gender estimation) are sought to solve distinct research problems through a variety of techniques [12][13][14][15][16][17]. The facial attribute information such as age and gender are already being predicted using facial landmark information [15][16][17][18][19][20][21]. Spotting accurate facial landmarks is in itself a challenging issue.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Real Age and Gender Estimation from Unconstrained Face Image towards Smart Store Customer Relationship Management

Islam

Baek

2021

Applied Sciences

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The COVID-19 pandemic markedly changed the human shopping nature, necessitating a contactless shopping system to curb the spread of the contagious disease efficiently. Consequently, a customer opts for a store where it is possible to avoid physical contacts and shorten the shopping process with extended services such as personalized product recommendations. Automatic age and gender estimation of a customer in a smart store strongly benefit the consumer by providing personalized advertisement and product recommendation; similarly, it aids the smart store proprietor to promote sales and develop an inventory perpetually for the future retail. In our paper, we propose a deep learning-founded enterprise solution for smart store customer relationship management (CRM), which allows us to predict the age and gender from a customer’s face image taken in an unconstrained environment to facilitate the smart store’s extended services, as it is expected for a modern venture. For the age estimation problem, we mitigate the data sparsity problem of the large public IMDB-WIKI dataset by image enhancement from another dataset and perform data augmentation as required. We handle our classification tasks utilizing an empirically leading pre-trained convolutional neural network (CNN), the VGG-16 network, and incorporate batch normalization. Especially, the age estimation task is posed as a deep classification problem followed by a multinomial logistic regression first-moment refinement. We validate our system for two standard benchmarks, one for each task, and demonstrate state-of-the-art performance for both real age and gender estimation.

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Landmark-Guided Local Deep Neural Networks for Age and Gender Classification

Cited by 12 publications

References 30 publications

Face Gender Recognition in the Wild: An Extensive Performance Comparison of Deep-Learned, Hand-Crafted, and Fused Features with Deep and Traditional Models

Face Gender Recognition in the Wild: An Extensive Performance Comparison of Deep-Learned, Hand-Crafted, and Fused Features with Deep and Traditional Models

A Lightweight Deep Convolutional Neural Network Model for Real-Time Age and Gender Prediction

Deep Learning Based Real Age and Gender Estimation from Unconstrained Face Image towards Smart Store Customer Relationship Management

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