GENDER AND AGE IDENTIFICATION OF INDIVIDUALS FROM FACIAL IMAGES USING MACHINE LEARNING AND DEEP LEARNING TECHNIQUES

Abstract

Using the UTKFace dataset, this study presents a novel design of Convolutional Neural Networks (CNNs) to accurately identify age and gender solely from facial imagery. A well-structured CNN model from scratch utilizes RGB images of the dataset by employing fully connected and convolutional layers, along with Adam optimizer for optimal performance and outstanding accuracy in gender prediction. Similarly, a comprehensive CNN model from scratch is also employed for age prediction after hyperparameter tuning, correctly identifying the distinct age categories with the implementation of cross-entropy loss and L2 regularization. Another model of Machine Learning called Support Vector Machine (SVM) has also been developed that utilizes Histogram Oriented Gradient (HOG) and Gabor Filters. The study focuses on dataset preparation, data preprocessing, model training, hyperparameter refining, and optimization and evaluation metrics. It also ensures the flexibility of the models through training, validation and testing on the popular UTK Face Dataset. With precise dataset curation and feature extraction, the techniques of ML and DL provide a solid base for the integration of the trained model to an application software interface to predict age and gender from facial images of the individuals in real time. Additionally, it also offers further opportunities for future developments and applications in other industries that depend on facial analysis information.