Face Identification Using a Near-Infrared Camera in a Nonrestrictive In-Vehicle Environment
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There are unrestricted conditions on the driver's face inside the vehicle, such as changes in lighting, partial occlusion and variouschanges in the driver's condition. In this paper, we propose a face identification system in an unrestricted vehicle environment. Theproposed method uses a near-infrared (NIR) camera to minimize the changes in facial images that occur according to the illuminationchanges inside and outside the vehicle. In order to process a face exposed to extreme light, the normal face image is changed to asimulated overexposed image using mean and variance for training. Thus, facial classifiers are simultaneously generated under both normaland extreme illumination conditions. Our method identifies a face by detecting facial landmarks and aggregating the confidence scoreof each landmark for the final decision. In particular, the performance improvement is the highest in the class where the driver wearsglasses or sunglasses, owing to the robustness to partial occlusions by recognizing each landmark. We can recognize the driver by usingthe scores of remaining visible landmarks. We also propose a novel robust rejection and a new evaluation method, which considers therelations between registered and unregistered drivers. The experimental results on our dataset, PolyU and ORL datasets demonstrate theeffectiveness of the proposed method.
Abstract
There are unrestricted conditions on the driver's face inside the vehicle, such as changes in lighting, partial occlusion and variouschanges in the driver's condition. In this paper, we propose a face identification system in an unrestricted vehicle environment. Theproposed method uses a near-infrared (NIR) camera to minimize the changes in facial images that occur according to the illuminationchanges inside and outside the vehicle. In order to process a face exposed to extreme light, the normal face image is changed to asimulated overexposed image using mean and variance for training. Thus, facial classifiers are simultaneously generated under both normaland extreme illumination conditions. Our method identifies a face by detecting facial landmarks and aggregating the confidence scoreof each landmark for the final decision. In particular, the performance improvement is the highest in the class where the driver wearsglasses or sunglasses, owing to the robustness to partial occlusions by recognizing each landmark. We can recognize the driver by usingthe scores of remaining visible landmarks. We also propose a novel robust rejection and a new evaluation method, which considers therelations between registered and unregistered drivers. The experimental results on our dataset, PolyU and ORL datasets demonstrate theeffectiveness of the proposed method.
참고문헌 (23)
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