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Paper WE-LA-T14.1

Laufer, Patrick (IAV Fahrzeugsicherheit), Gühmann, Clemens (Technische Universität Berlin)

Vehicle Occupant Age and Gender Estimation Using Convolutional Neural Networks on RGB and NIR Images

Scheduled for presentation during the Regular Session "S14c-Human Factors and Human Machine Interaction in Automated Driving" (WE-LA-T14), Wednesday, November 19, 2025, 16:00−16:20, Currumbin

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 19, 2025

Keywords Human-Machine Interaction Systems for Enhanced Driver Assistance and Safety, Autonomous Vehicle Safety and Performance Testing

Abstract

To enhance safety within the vehicle interior, the New Car Assessment Protocol (NCAP) emphasizes the importance of estimating occupant features, such as age and gender, utilizing integrated sensors like vehicle interior cameras. This work presents a data augmentation strategy designed to enhance the performance of a Convolutional Neural Network (CNN) in estimating the facial age and gender of both the driver and co-driver from vehicle interior images captured in near-infrared (NIR) and visual (RGB) modalities. The approach was initially tested on publicly available datasets and later evaluated using an in-house dataset derived from a study involving 50 unique test subjects, who acted as drivers and co-drivers within a car. In the in-house dataset, facial images were generated using both coarse and tight cropping strategies to identify the optimal approach. Results indicate that the proposed data augmentation strategy, combined with tighter cropping, significantly enhances gender and age estimation performance in both RGB and NIR modalities while outperforming state of the art methods.

 

 

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