A hybrid deep learning CNN–ELM for age and gender classification

Human face image analysis is an active research area within computer vision. In this paper we propose a framework for face image analysis, addressing three challenging problems of race, age, and gender recognition through face parsing. We manually labeled face images for training an end-to-end face parsing model through Deep Convolutional Neural Networks. The deep learning-based segmentation model parses a face image into seven dense classes. We use the probabilistic classification method and created probability maps for each face class. The probability maps are used as feature descriptors. We trained another Convolutional Neural Network model by extracting features from probability maps of the corresponding class for each demographic task (race, age, and gender). We perform extensive experiments on state-of-the-art datasets and obtained much better results as compared to previous results.

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“…Adience RAG-MCFP-DCNNs 69.4 Dehghan et al [87] 61.3 Hou et al [88] 61.1 Hassner et al [89] 50.7 Hernandez et al [90] 51.6 CNN-ELM [73] 52.3…”

Section: Methods Classification Aaccuracy (%)mentioning

confidence: 99%

“…The three demographic attributes (race, age, and gender) were also explored in a single model through these deep learning architectures. For example, a hybrid approach for age and gender was introduced in [73]. DCNNs were used for features extraction and for classification extreme machine learning (EML) strategy was adapted.…”

Section: Multi Tasks Frameworkmentioning

confidence: 99%

A Multi-Task Framework for Facial Attributes Classification through End-to-End Face Parsing and Deep Convolutional Neural Networks

Khan

Attique

Khan

et al. 2020

Sensors

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“…Compared to the aforementioned networks, the pretrained networks; GoogleNet [6], AlexNet [25] are deeper in terms of layer that produces good results mostly on the applied cases. A hybrid system for gender and age classification was presented in [26]. However, most of these methods were evaluated on the constrained imaging conditions.…”

Section: Background and Related Workmentioning

confidence: 99%

Human Gender Classification Using Transfer Learning via Pareto Frontier CNN Networks

2020

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Human gender is deemed as a prime demographic trait due to its various usage in the practical domain. Human gender classification in an unconstrained environment is a sophisticated task due to large variations in the image scenarios. Due to the multifariousness of internet images, the classification accuracy suffers from traditional machine learning methods. The aim of this research is to streamline the gender classification process using the transfer learning concept. This research proposes a framework that performs automatic gender classification in unconstrained internet images deploying Pareto frontier deep learning networks; GoogleNet, SqueezeNet, and ResNet50. We analyze the experiment with three different Pareto frontier Convolutional Neural Network (CNN) models pre-trained on ImageNet. The massive experiments demonstrate that the performance of the Pareto frontier CNN networks is remarkable in the unconstrained internet image dataset as well as in the frontal images that pave the way to developing an automatic gender classification system.

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“…At the same time, they only employed local receptive fields and do not fully utilized the feature extraction capability of convolutional layers. Duan et al [38] introduced a hybrid deep learning CNN-ELM method. By combining CNN and ELM in a hybrid recognition architecture, they exploited the excellent feature representation of CNN and the fast inference speed of ELM.…”

Section: Related Workmentioning

confidence: 99%

FnnmOS-ELM: A Flexible Neural Network Mixed Online Sequential Elm

et al. 2019

Applied Sciences

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The learning speed of online sequential extreme learning machine (OS-ELM) algorithms is much higher than that of convolutional neural networks (CNNs) or recurrent neural network (RNNs) on regression and simple classification datasets. However, the general feature extraction of OS-ELM makes it difficult to conveniently and effectively perform classification on some large and complex datasets, e.g., CIFAR. In this paper, we propose a flexible OS-ELM-mixed neural network, termed as fnnmOS-ELM. In this mixed structure, the OS-ELM can replace a part of fully connected layers in CNNs or RNNs. Our framework not only exploits the strong feature representation of CNNs or RNNs, but also performs at a fast speed in terms of classification. Additionally, it avoids the problem of long training time and large parameter size of CNNs or RNNs to some extent. Further, we propose a method for optimizing network performance by splicing OS-ELM after CNN or RNN structures. Iris, IMDb, CIFAR-10, and CIFAR-100 datasets are employed to verify the performance of the fnnmOS-ELM. The relationship between hyper-parameters and the performance of the fnnmOS-ELM is explored, which sheds light on the optimization of network performance. Finally, the experimental results demonstrate that the fnnmOS-ELM has a stronger feature representation and higher classification performance than contemporary methods.

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A hybrid deep learning CNN–ELM for age and gender classification

Cited by 214 publications

References 36 publications

A Multi-Task Framework for Facial Attributes Classification through End-to-End Face Parsing and Deep Convolutional Neural Networks

A Multi-Task Framework for Facial Attributes Classification through End-to-End Face Parsing and Deep Convolutional Neural Networks

Human Gender Classification Using Transfer Learning via Pareto Frontier CNN Networks

FnnmOS-ELM: A Flexible Neural Network Mixed Online Sequential Elm

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