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http://dx.doi.org/10.3745/KTSDE.2021.10.3.99

Face Identification Using a Near-Infrared Camera in a Nonrestrictive In-Vehicle Environment  

Ki, Min Song (연세대학교 컴퓨터과학과)
Choi, Yeong Woo (숙명여자대학교 소프트웨어학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.10, no.3, 2021 , pp. 99-108 More about this Journal
Abstract
There are unrestricted conditions on the driver's face inside the vehicle, such as changes in lighting, partial occlusion and various changes in the driver's condition. In this paper, we propose a face identification system in an unrestricted vehicle environment. The proposed method uses a near-infrared (NIR) camera to minimize the changes in facial images that occur according to the illumination changes inside and outside the vehicle. In order to process a face exposed to extreme light, the normal face image is changed to a simulated overexposed image using mean and variance for training. Thus, facial classifiers are simultaneously generated under both normal and extreme illumination conditions. Our method identifies a face by detecting facial landmarks and aggregating the confidence score of each landmark for the final decision. In particular, the performance improvement is the highest in the class where the driver wears glasses or sunglasses, owing to the robustness to partial occlusions by recognizing each landmark. We can recognize the driver by using the scores of remaining visible landmarks. We also propose a novel robust rejection and a new evaluation method, which considers the relations between registered and unregistered drivers. The experimental results on our dataset, PolyU and ORL datasets demonstrate the effectiveness of the proposed method.
Keywords
Face Identification; Near-infrared Image; Multi Support Vector Machine (Multi-SVM); Light Overexposure;
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