Gender classification of full body images based on the convolutional neural network

Yu, Zhiwu; Shen, Chengxuan; Chen, Lin

doi:10.1109/spac.2017.8304366

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…In the MIT dataset, accuracy rates of 82.9%, 81.8%, and 82.4%, as well as about 91.5% in the PETA dataset, have been attained for the anterior, posterior, and mixed views, respectively. Also (Yu et al in 2017), proposed a CNN model with few layers and minimal complexity. On a dataset of 1496 whole body pictures, our technique has an accuracy rate of 91.5%.…”

Section: Deep Learningmentioning

confidence: 99%

Age and Gender Identification through Advanced Deep Learning Techniques

Nabi,

Mufaddhal,

Khai

et al. 2023

MyJICT

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The ability to automatically detect the gender and age of an individual based on their facial features or other attributes is an important task in computer vision and deep learning. The precision and dependability of gender and age detection models have significantly increased with the use of deep learning algorithms and massive datasets. The lack of diversity in the training data, which can result in prejudice and subpar performance on members of underrepresented groups, is only one of several issues that still need to be resolved. Deep learning for gender and age detection also poses ethical issues including the possibility of abuse and prejudice. The goal of this work is to overcome these difficulties and create models that are very accurate at identifying a person's gender and age. The study's objectives include incorporating ethical issues into the creation and use of the models as well as enhancing the models' effectiveness on members of underrepresented groups. The model will also be optimized for real-time uses in this study, including security and marketing.

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Section: Deep Learningmentioning

confidence: 99%

Age and Gender Identification through Advanced Deep Learning Techniques

Nabi,

Mufaddhal,

Khai

et al. 2023

MyJICT

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“…For gender categorization, this technique has an accuracy of 89.65%. In (Yu et al, 2017) researchers proposes a CNN with reduced number of layers. By applying the method on a dataset composed of 1496 body images, it achieves 91.5% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Gender Classification from Eye Images by Using Pretrained Convolutional Neural Networks

CIMTAY¹,

UNIVERSITY²

2021

EPSTEM

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Automatic gender classification from face images has been a popular topic among researchers for a decade. Feature extraction and classification methods are very important to create a successful automatic classification system. Due to the richness of face image datasets today, many successful machine learning and deep learning methods has been implemented. It is very critical to extract accurate features from the datasets to achieve promising classification scores when traditional machine learning methods are used. However, deep learning models have been designed to extract the features automatically from the raw data directly. This also automatize the feature extraction process besides classification. The hidden and unpredictable feature sets can be explored by the deep neural networks which can increase the classification performance comparing to traditional machine learning methods. Convolutional Neural Networks (CNN) as one of the effective classes of deep models have been adopted by many scientists for solving the gender classification problem. It can solve the problem of the fact that facial cues can change from origin to origin which makes the accurate feature extraction harder. There are several state-of-the art pretrained CNN structures which are very successful for image classification problems. The performance of CNNs is generally higher when the number of the input data is high. However, in this study, the success of the pretrained CNN models is investigated when the data is limited. Considering this fact, in this study, rather than using complete face images, only the one eye image regions with eyebrows are used for the gender classification. The performance results present that the best CNN models are NASNetLarge and Xception models.

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Gender Classification Accuracy via Two-Dimensional Body Joints using Convolutional Neural Networks

Sung,

Attar

2023

2023 IEEE International Conference on Big Data (BigData)

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Gender classification of full body images based on the convolutional neural network

Cited by 6 publications

References 14 publications

Age and Gender Identification through Advanced Deep Learning Techniques

Age and Gender Identification through Advanced Deep Learning Techniques

Gender Classification from Eye Images by Using Pretrained Convolutional Neural Networks

Gender Classification Accuracy via Two-Dimensional Body Joints using Convolutional Neural Networks

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