Exploring facial attribute inference with ResNet: a study on head pose estimation and gender prediction
Ruihang Zhang · 2024
This paper explores the realms of face recognition through the lenses of head pose estimation and gender prediction using deep learning architectures such as ResNet and InceptionResnetV1. Our investigation into head pose estimation involved training a model to predict the three-dimensional orientation of human heads. Concurrently, we delved into gender prediction, constructing a model that accurately discerns the gender of individuals depicted in images through feature extraction and clustering techniques. Our findings contribute to the advancement of Facial recognition techniques, with implications for various applications such as human-computer interaction, demographic analysis, and targeted advertising.