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Improvement of Active Shape Model for Detecting Face Features in iOS Platform  

Lee, Yong-Hwan (Dept. of Smart Mobile, Fat East University)
Kim, Heung-Jun (Dept. of Computer Science and Engineering, Gyeongnam National University of Science and Technology)
Publication Information
Journal of the Semiconductor & Display Technology / v.15, no.2, 2016 , pp. 61-65 More about this Journal
Abstract
Facial feature detection is a fundamental function in the field of computer vision such as security, bio-metrics, 3D modeling, and face recognition. There are many algorithms for the function, active shape model is one of the most popular local texture models. This paper addresses issues related to face detection, and implements an efficient extraction algorithm for extracting the facial feature points to use on iOS platform. In this paper, we extend the original ASM algorithm to improve its performance by four modifications. First, to detect a face and to initialize the shape model, we apply a face detection API provided from iOS CoreImage framework. Second, we construct a weighted local structure model for landmarks to utilize the edge points of the face contour. Third, we build a modified model definition and fitting more landmarks than the classical ASM. And last, we extend and build two-dimensional profile model for detecting faces within input images. The proposed algorithm is evaluated on experimental test set containing over 500 face images, and found to successfully extract facial feature points, clearly outperforming the original ASM.
Keywords
Face Feature Extraction; Active Shape Model; Face Detection; iOS Platform;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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