| Summary: | 碩士 === 國立臺北科技大學 === 資訊工程系研究所 === 99 === Recently, there are many studies focus on the eye detection based on the computer vision technology. However, the eye detection system in the practical usage should work in the complex environment robustly and correctly. Besides, the efficiency of the eye detection system is the critical issue in order to achieve the system in real time. The eye detection system must overcome the complex background, the uneven brightness, the overall environmental illumination, and the different camera angles problems. These conditions will seriously affect the detection rate and detection time.
Therefore, we present a low cost real-time eye detection which conquers the non-uniform and complex environmental illumination. The proposed framework can be divided into the following five technologies: the image enhancement technology, the reduction of the impact of uneven illumination technology, the face detection technology, and the eye detection technology. We use the Retinex algorithm to eliminate the impact of light, and then use the Haar-like features with AdaBoost learning algorithm to achieve face and eye detection. The proposed method is also implemented on the embedded system in real time. The experimental results demonstrate that the detection accuracy is over 90% with 10 ~ 20fps frame rate.
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