Open AccessGender Recognition System Using Convolutional Neural Network
Author(s) -
K. Bhavana,
K. Sravya Reddy,
Bommiga Pranathi,
CH. Maheshwari,
T. Ratnamala
Publication year - 2022
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-3137
Subject(s) - artificial intelligence , computer science , convolutional neural network , pattern recognition (psychology) , feature extraction , classifier (uml) , face (sociological concept) , convolution (computer science) , computer vision , pooling , contextual image classification , facial recognition system , image processing , feature (linguistics) , face detection , artificial neural network , image (mathematics) , social science , linguistics , philosophy , sociology
Human gender detection which is a part of facial recognition has received extensive attention because of it’s different kinds of application. Previous research works on gender detection have been accomplished based on different static body feature for example face, eyebrow, hand-shape, body-shape, finger nail etc. In this research work, we have presented human gender classification using Convolution Neural Network (CNN) from human face images as CNN has been recognised as best algorithm in the field of image classification. To implement our system, at first a pre-processing technique has been applied on each image using image processing. The pre-processed image is passed through the Convolution, RELU and Pooling layer for feature extraction. A fully connected layer and a classifier is applied in the classification part of the image. To obtain a better result, we have implemented our system using different optimizers. We use libraries like , Keras , Opencv and also uses Tensorflow as backend.