Predicting age and gender with the caffe deep learning framework.

The rise in social media usage has highlighted the need for accurate automatic age and gender classification in images. However, current methods often struggle with real-world photos, unlike the significant advancements seen in facial recognition. This paper proposes a deep learning approach by using Convolutional Neural Networks (CNNs) method to enhance the performance. The main aim of this study is to design the system which is capable of effectively predicting a person's age and gender. Traditionally, Haar cascades were widely used for gender detection. The proposed model is trained on a diverse dataset comprising male and female images, categorizing them as positive or negative examples. By extracting facial features, Haar Cascade can leverage the model for gender classification even with limited data. For age and gender estimation, we utilize a deep learning framework constructed with Caffe. This robust framework enables our model to identify multiple faces within a single image and forecast the age and gender for each individual. [ABSTRACT FROM AUTHOR]