Inferencing Age and Gender from Facial Images

The increasing use of social media and platforms has made automatic age and gender classification a crucial task for a variety of applications. Still, current approaches frequently perform less than optimally when faced with real-world image data, especially when compared to the significant advancements shown in face recognition tasks. Here, we demonstrate how deep convolutional neural networks (CNNs) can learn effective representations and hence considerably improve performance on certain tasks. We present a simplified convolutional network design that remains effective in situations where training material is scarce. We use strict preprocessing methods to extract gender and age relevant characteristics from CNNs, and then we use specialized classifiers to classify the images. Our empirical findings demonstrate the greater accuracy and resilience of our suggested methodology over the most recent techniques, especially when applied to real-world datasets. We believe that this will contribute to the advancement of automatic age and gender classification systems, with potential applications in various domains such as social media analytics, marketing research, and personalized content recommendation.