Authors: Gayatri Solanki, Abhay Mundra
Abstract: Gender identification from facial images is an important problem in computer vision and has attracted growing interest due to its applications in surveillance, security, and human-centered systems. Although humans can infer gender naturally, developing automated systems that perform reliably across real-world conditions remains challenging. This work presents a gender classification framework that leverages face recognition feature vectors for prediction. First, face images are detected, aligned, and preprocessed to obtain a standardized facial representation. Next, a face recognition network extracts compact feature embeddings that encode discriminative facial characteristics. Finally, machine learning and deep learning classifiers are applied to these embeddings to determine gender. The proposed system integrates advanced components including VGG-Face, Deep Belief Networks, and shifted filter responses to improve robustness. Multiple deep learning architectures were investigated—CNN, VGG16, ResNet50, InceptionV3, and EfficientNet—with ResNet152 showing the strongest overall performance. Experimental findings indicate that ResNet152 achieves approximately 9% improvement over leading alternatives and demonstrates enhanced resilience to anomalies and variations compared with earlier approaches.
