APPLICATION OF GENDER CLASSIFICATION USING CNN MODEL WITH PYTHON PROGRAM

Abstract

For the past ten years, researchers have found great interest in the automatic classification of gender from face photos. For an automatic classification system to be successful, feature extraction and classification techniques are crucial. Today's rich face image databases have made it possible to apply numerous effective machine learning and deep learning techniques. When using classical machine learning methods, it is crucial to extract precise features from the datasets in order to produce promising classification results. On the other hand, features can be automatically extracted from raw data directly using deep learning models. In addition to classifying, this automates the feature extraction procedure. When compared to conventional machine learning techniques, deep neural networks can improve classification performance by exploring hidden and unpredictable feature sets. Many scientists have turned to convolutional neural networks (CNN), one of the most successful types of deep models, to help them solve the gender classification challenge. It can address the issue of face cues changing from one origin to another, which complicates precise feature extraction. Modern pretrained CNN architectures come in several forms that work incredibly well for picture categorization issues. In general, CNNs perform better when there are more input data points. In this study, we classified the images according to gender like Male, Female, Baby, and Girl. We trained the CNN model to classify the images using a Python program. Multi-class image classification is a computer vision task that involves categorizing images into more than two classes or categories. In other words, the goal is to teach a machine learning model to recognize and distinguish between multiple classes or types of objects within images.