Genderpredictions using Convolution Neural Networks
Kurshid Madina1, Saksham Mansotra2
1KurshidMadina, B.Tech in Information Technology from GITAM Institute of Technology, Visakhapatnam.
2Saksham Mansotra, B.Tech in Information Technology from GITAM Institute of Technology, Visakhapatnam.
Manuscript received on August 01, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on September 30, 2020. | PP: 537-540 | Volume-9 Issue-3, September 2020. | Retrieval Number: 100.1/ijrte.C4606099320 | DOI: 10.35940/ijrte.C4606.099320
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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: Nowadays Deep learning was advanced so much in our daily life. From 2014, there is massive growth in this technology as there is a vast amount of data present. We are even getting better results from whatever we may do. In my work, I have used Convolution Neural Networks as my project depends on image classification. So what I’m trying to do is I’m using two classes in which one class is male and one class is female. I’m classifying both the classes and trying to predict who is male and who is female. For this, I have been using layers like Sequential, Convolution2D, Max-pooling, Flattening, and finally Dense. So, I connect all of these layers. I have been using two more extra layers which are Convolution2D and max-pooling connected as one layer for better classifications. In my model, I’m using Adam optimizer as I’m having only two classes and in my experiments, I found Adam as a good optimizer and I use binary cross entropy as my loss function as I’m using only two classes if we have more than two classes we can use categorical loss function and the images which I use for predictions will be converted into 64*64 matrix form. In the end, I will be getting predictions as 1 for male and 0 for female.
Keywords: Computer Vision, Gender Classification, Human-computer interaction, Convolution Neural Network (CNN).