Age Prediction using Image Dataset using Machine Learning
Ishita Verma1, Urvi Marhatta2, Sachin Sharma3, Vijay Kumar4
1Ishita Verma, Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun (Uttarakhand), India.
2Urvi Marhatta, Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun (Uttarakhand), India.
3Sachin Sharma, Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun (Uttarakhand), India.
3Vijay Kumar, Department of Physics, Graphic Era Hill University, Dehradun (Uttarakhand), India.
Manuscript received on 16 June 2020 | Revised Manuscript received on 27 June 2020 | Manuscript Published on 04 July 2020 | PP: 107-113 | Volume-8 Issue-12S3 October 2019 | Retrieval Number: L102010812S319/2020©BEIESP | DOI: 10.35940/ijitee.L1020.10812S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Gender is a central feature of our personality still. In our social life it is also an significant element. Artificial intelligence age predictions can be used in many fields, such as smart human-machine interface growth , health, cosmetics, electronic commerce etc. The prediction of people’s sex and age from their facial images is an ongoing and active problem of research. The researchers suggested a number of methods to resolve this problem, but the criteria and actual performance are still inadequate. A statistical pattern recognition approach for solving this problem is proposed in this project. Convolutionary Neural Network (ConvNet / CNN), a Deep Learning algorithm, is used as an extractor of features in the proposed solution. CNN takes input images and assigns value to different aspects / objects (learnable weights and biases) of the image and can differentiate between them. ConvNet requires much less preprocessing than other classification algorithms. While the filters are hand-made in primitive methods, ConvNets can learn these filters / features with adequate training. In this research, face images of individuals have been trained with convolutionary neural networks, and age and sex with a high rate of success have been predicted. More than 20,000 images are containing age, gender and ethnicity annotations. The images cover a wide range of poses, facial expression, lighting, occlusion, and resolution.
Keywords: Facial Images; Gender Prediction; Age Prediction; Convolutional Neural Network; Deep Learning.
Scope of the Article: Machine Learning