Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Face Recognition using Non-negative Matrix Factorization and Learning Vector Quantization

비음수 행렬 분해와 학습 벡터 양자화를 이용한 얼굴 인식

  • Jin, Donghan (Dept. of Information and Telecommunication Eng., Incheon National University) ;
  • Kang, Hyunchul (Dept. of Information and Telecommunication Eng., Incheon National University)
  • 진동한 (인천대학교 정보통신공학과) ;
  • 강현철 (인천대학교 정보통신공학과)
  • Received : 2016.11.10
  • Accepted : 2017.02.16
  • Published : 2017.03.25
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Abstract

Non-negative matrix factorization (NMF) is one of the typical parts-based representation in which images are expressed as a linear combination of basis vectors that show the lcoal features or objects in the images. In this paper, we represent face images using various NMF methods and recognize their face identities based on extracted features using a learning vector quantization. We analyzed the various NMF methods by comparing extracted basis vectors. Also we confirmed the availability of NMF to the face recognition by verification of recognition rate of the various NMF methods.

비음수 행렬 분해 기법(non-negative matrix factorization)은 대표적인 부분 영역 기반 표현 기법의 하나로 영상의 부분적인 특징을 나타내는 기저 벡터의 선형 조합으로 영상을 표현하는 기법이다. 본 논문에서는 여러 가지 비음수 행렬 분해 기법을 이용하여 얼굴 영상을 표현하고, 추출된 특징을 기반으로 학습 벡터 양자화를 이용하여 얼굴 인식을 수행하였다. 추출된 각 기법의 기저 벡터를 비교하여 각 기법의 특징을 분석하였다. 또한 NMF 기법들의 인식율 검증을 통해 비음수 행렬 기법의 얼굴 인식에 대한 활용 가능성을 확인하였다.

Keywords

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