Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
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Improvement of Active Shape Model for Detecting Face Features in iOS Platform

iOS 플랫폼에서 Active Shape Model 개선을 통한 얼굴 특징 검출

  • 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)
  • 이용환 (극동대학교 스마트모바일학과) ;
  • 김흥준 (경남과학기술대학교 컴퓨터공학과)
  • Received : 2016.06.02
  • Accepted : 2016.06.22
  • Published : 2016.06.30
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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

References

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