Abstract
This research is about constructing a novel close-distance eye-line detection technique as a part of an automatic eye-line makeup system. Based on the inspection of the characteristics of the images, the proposed eye-line detection method includes the following two steps: eye-line contour tracing and eye-line end-point detection.For the eye-line contour tracing, due to the photo capturing mechanism of the device, the scale between the sizes of the captured and the real eye-line is close to 1:1. This results in an amplification effect of the noisy region (e.g. wrinkles or freckles) around the eye-line. This also magnifies the thickness of the eye-line so that we treat it as a region rather than a line. We therefore propose the use of image shifting and subtraction based on the color and eye-line orientation properties. We then perform a contour tracing technique based on pixel intensity projection and sub-image shifting.We use an eye-line end-point detection method as the stopping criterion for contour tracing. We examine the performance of an unsupervised method (k-means) and three supervised learning methods. Three types of feature are used based on intensity, local binary pattern, and Haar wavelet, respectively. The supervised learning methods under inspection include support vector machine, nearest-neighbor rule, and cascade adaptive boosting.In the end-point detection experiment, we use Euclidean distance to measure the accuracy. The experimental result shows that support vector machine with intensity-based feature can achieve the best accuracy 96% within 3.8mm tolerance (suppose the length of a real eye-line is around 3.5 cm). For the overall performance of the system, we use the Hausdorff distance to measure the similarity between the detected eye-line and the real eye-line. The experimental result shows that an acceptable accuracy is achieved within a reasonable tolerance. On the other hand, we adopt a safety mechanism by detecting whether the eye is opened or closed in real time using template matching and Hough transform.