Deep Learning-Based Prediction of Age and Gender from Facial Images

Abstract

The automated prediction of age and gender using facial images is gaining traction in various real-world applications, including social media platforms, surveillance systems, and medical fields. This study primarily focuses on automatic gender classification, a critical research domain with substantial potential in systems pertaining to computer vision, biometric authentication, credit card verification, visual surveillance, demographic data gathering, and security. Despite the apparent ease with which humans discern gender by facial observation, replicating this process in computers is challenging due to diverse variables such as illumination, facial expressions, head pose, age, image scale, camera quality, and facial part occlusion. Thus, an effective computer-based system necessitates meaningful data or discriminative features for accurate identification. Over the years, automated facial recognition, along with gender and age estimation using Artificial Intelligence (AI), has been the subject of extensive research. This paper presents a comprehensive summary of the technical aspects of the Deep Convolutional Neural Network (DCNN) architecture, emphasizing key concepts and potential algorithms for predictive applications. The primary aim of this research is to devise and analyze an expression-invariant gender classification algorithm. This algorithm is founded on the fusion of image intensity variation, shape, and texture features, extracted from various scales of facial images using a block processing technique. Looking ahead, our proposed system could potentially be extended for medical analyses, offering personalized medication and nutritional recommendations based on individual gender and age factors. Such an expansion could herald a new era in personalized healthcare, underscoring the importance of our research.