Exploring facial attribute inference with ResNet: a study on head pose estimation and gender prediction

Ruihang Zhang

doi:10.1117/12.3034899

7 August 2024 Exploring facial attribute inference with ResNet: a study on head pose estimation and gender prediction

Ruihang Zhang

Proceedings Volume 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024); 132241U (2024) https://doi.org/10.1117/12.3034899
Event: 4th International Conference on Internet of Things and Smart City, 2024, Hangzhou, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ruihang Zhang "Exploring facial attribute inference with ResNet: a study on head pose estimation and gender prediction", Proc. SPIE 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024), 132241U (7 August 2024); https://doi.org/10.1117/12.3034899

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