International Journal of Progressive Research in Engineering Management and Science
(Peer-Reviewed, Open Access, Fully Referred International Journal)

ISSN:2583-1062
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Paper Details

Age and Gender Prediction Using Deep Learning (KEY IJP************674)

  • Tamilselvi K B,Sneka S,Sowmya M,Vaishnavi M,Sathiya S

Abstract

The last decade or two has witnessed a boom of images. With the increasing ubiquity of cameras and with the advent of selfies, the number of facial images available in the world has sky rocketed. Consequently, there has been a growing interest in automatic age and gender predict using facial images. Feature extraction involves extracting relevant features from the facial images, such as facial landmarks, texture, and color information. These features can be used to train machine learning models to predict age and gender. Model training involves using machine learning algorithms such as linear regression, neural networks, and support vector machines (SVMs) to create a model that can predict age and gender from the extracted features.We in this paper focus on this challenging problem. Specifically, this paper focuses on age estimation, age classification and gender classification from still facial images of an individual. We train different models for each problem and we also draw comparisons between build-ing a custom CNN (Convolutional Neural Network) architecture and using various CNN architectures as feature extractors, namely openCV-trained on VGGFace, Res-Net50 and SE-ResNet50 pre-trained on VGGFace2 dataset and training over those ex-tracted features. We also provide baseline performance of various machine learning algorithms on the feature extraction which gave us the best results.

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