Age and Gender Prediction using Face Recognition
Sai Teja Challa1, Sowjanya Jindam2, Ruchitha Reddy Reddy3, Kalathila Uthej4
1Sai Teja Challa*, Department of Bachelor of Engineering, Maturi Venkata Subba Rao Engineering College, Osmania University Hyderabad (Telangana) India.
2Sowjanya Jindam, Department of Bachelor of Engineering, Maturi Venkata Subba Rao Engineering College, Osmania University Hyderabad (Telangana) India.
3Ruchitha Reddy Reddy, Department of Bachelor of Engineering, Maturi Venkata Subba Rao Engineering College, Osmania University Hyderabad (Telangana) India.
4Kalathila Uthej, Department of Bachelor of Engineering, Maturi Venkata Subba Rao Engineering College, Osmania University Hyderabad (Telangana) India.
Manuscript received on November 15, 2021. | Revised Manuscript received on November 17, 2021. | Manuscript published on December 30, 2021. | PP: 48-51 | Volume-11 Issue-2, December 2021. | Retrieval Number: 100.1/ijeat.B32751211221 | DOI: 10.35940/ijeat.B3275.1211221
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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: Automatic age and gender prediction from face images has lately attracted much attention due to its wide range of applications in numerous facial analyses. We show in this study that utilizing the Caffe Model Architecture of Deep Learning Frame Work; we were able to greatly enhance age and gender recognition by learning representations using deep-convolutional neural networks (CNN). We propose a much simpler convolutional net architecture that can be employed even if no learning data is available. In a recent study presenting a potential benchmark for age and gender estimation, we show that our strategy greatly outperforms existing state-of-the-art methods.
Keywords: Caffe model, Deep-Convolutional neural networks, Deep Learning framework, Tensor Flow Abbreviations and Acronyms: CNN- Convolutional Neural Networks DNN-Deep Neural Network CV- Computer Vision.
Scope of the Article: Image Processing and Pattern Recognition