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A wide variety of present approaches work well in detecting frontal faces, but they are often unable to detect faces with partial occlusion, rotation and strong shadows. To address this problem, we propose an efficient technique. First, we filter the face-like regions from the input image using skin-color model. And then component classifiers are used to detect the faces' components from these potential...
A new automated mechanism named save-fail for validating face detection is proposed in this paper in order to determine whether or not there does exist faces in the candidate region which was located by the AdaBoost face detection algorithm. The mechanism is based on one simple fact, every face has two eyes and a nose, and human eyes and nose has specified features that can be used to distinguish...
We present a feature-based method to classify salient points as belonging to objects in the face or background classes. We use SURF local descriptors (speeded up robust features) to generate feature vectors and use SVMs (support vector machines) as classifiers. Our system consists of a two-layer hierarchy of SVMs classifiers. On the first layer, a single classifier checks whether feature vectors are...
Face detection has advanced dramatically over the past three decades. Algorithms can now quite reliably detect faces in clutter in or near real time. However, much still needs to be done to provide an accurate and detailed description of external and internal features. This paper presents an approach to achieve this goal. Previous learning algorithms have had limited success on this task because the...
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