Hybrid CNN-RNN Model for Accurate Image Captioning with Age and Gender Detection
B.S. Utkars Jain, Khushi Doshi, Pulkit Dwivedi · 2023
Age and gender detection is a computer vision process that aims to identify the age and gender of individuals based on their physical characteristics, primarily facial features. This research paper presents a novel approach to age and gender detection using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Our proposed method combines CNN and RNN models to accurately detect age and gender from input images. To address the limitations of existing datasets and the scarcity of data, we have developed a unique dataset that includes diverse facial images annotated with age and gender information. Our approach leverages CNN to extract region-based visual features from input images, while the RNN-based model decodes output captions word by word. Attention mechanisms are incorporated to highlight relevant regions in the input image during the detection process. Our results demonstrate high accuracy rates for both age and gender detection, showcasing the potential of the proposed method in various domains such as security, healthcare, and entertainment. This study offers a promising solution to the age and gender detection challenge, underscoring the effectiveness of CNN and RNN models in image analysis.