"Fast and Fully Automatic Ear Detection Using Cascaded AdaBoost" by Islam et al.: Used AdaBoost with custom weak learners to detect ears. The weak learners use custom features, including a center-surround feature used to detect ear pits. To improve efficiency, they actually use a cascade of AdaBoost classifiers, where the early classifiers should be quick to evaluate.
They get essentially perfect detection performance.
"Facial features segmentation by model-based snakes" by Radeva: Goal is segmentation of facial features, including eyes, eyebrows, and mouth using aligned images. They find the eyes using template matching. They then project the image along the x-axis, and look for a valley in the grayscale values indicating the eyebrow location (eyebrows are dark); this determines the eyebrow y location. To determine the x location, they project along the y-axis and use the fact that eyebrows are above eyes and both are dark, again looking for a valley in the grayscale values.
They use the approximate eyebrow locations to initialize an active-contour-like technique called a "rubber snake". The final position of the rubber snake provides the segmentation.
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