Investigating Image Histograms using CNN and Tensor Flow-Based Gender Classification

Abstract

This study investigates the integration of image histograms with Convolutional Neural Networks (CNNs) using TensorFlow for gender classification. The research focuses on preprocessing techniques that significantly reduce the dimensionality of image data, enhancing computational efficiency model performance. Data augmentation methods, including rotation, shifting, and flipping, were applied to diversify the training dataset. The CNN model achieved high accuracy and validation accuracy, demonstrating its robustness. The findings reveal that the preprocessing steps effectively condensed the pixel to be 151,321 while retaining critical features for classification. The study underscores the potential applications of this methodology in security, marketing, and healthcare, where accurate gender classification is essential. Future research should explore more diverse datasets, advanced model architectures, and enhanced feature extraction methods to further improve performance. This research contributes to the field by offering a comprehensive approach to efficient and accurate gender classification, supported by robust data augmentation and preprocessing techniques.