Real-Time Gender Classification from Facial Images Using VGG16 and GoogLeNet Architectures

Facial attribute recognition, particularly gender classification, has gained unprecedented interest in various fields like security, personalized marketing, and human-computer interaction in recent years. In this research, we dive into the deep learning realm and use transfer learning-based models for real-time gender classification from facial images. We use both VGG16 (Visual Geometry Group 16-layer network) and GoogLeNet structures as our feature extraction backbone, with pre-trained weights on ImageNet to exploit the tremendous results obtained on relatively small and targeted datasets. Robust real-world variability is made through heavy use of data augmentation techniques such as rotation, scaling, and flipping. The preprocessed dataset feeds into the models whose optimized hyperparameters incur both accuracy and efficiency. Experimental results show that although the VGG16 and GoogLeNet models offer high accuracy and adaptability for gender classification tasks, each of their architectures possesses a different strength in processing speed and feature representation. The paper gives insights on the comparative performance of these architectures in the context of gender classification with potential applications in a myriad number of fields that require rapid and reliable demographic profiling.