Facial feature point detection
This chapter describes an automated technique for detecting the eighteen most important facial feature points using a statistically developed anthropometric face model. Most of the important facial feature points are located just about the area of mouth, nose, eyes and eyebrows. After carefully observing the structural symmetry of human face and performing necessary anthropometric measurements, we have been able to construct a model that can be used in isolating the above mentioned facial feature regions. In the proposed model, distance between the two eye centers serves as the principal parameter of measurement for locating the centers of other facial feature regions.
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With Aspects of Artificial Intelligence pp Cite as. This paper presents an algorithm which detects automatically the feature points in a face image. This is a fundamental task in many applications, in particular in an automatic face recognition system. Starting from a frontal face image with a plain background we have effected an image segmentation to detect the different facial components eyebrow, eyes, nose, mouth and chin. After this we have searched for the feature points of each face component.
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We explore how to leverage the performance of face feature points detection on mobile terminals from 3 aspects. Second, we propose an evaluation criterion using Linear Discriminative Analysis to choose the best local feature descriptions which plays a critical role in feature points detection. Third, we take advantage of multicore architecture of mobile terminal and parallelize the optimized SDM algorithm to improve the efficiency further.
Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Automatic detection of facial feature points in image sequences Abstract: Detection and location of the face as well as extraction of facial features from images is an important stage for numerous facial image interpretation tasks.