Earticle

초록

영어

In modern life, we need better techniques based on biometric features recognition such as face recognition, fingerprint recognition and iris recognition. We present a method which can be used for face recognition or verification applications. The method can solve the problem that when the number of data categories is large and each number of the category used for training is small. As the conventional four stages, face detection, face alignment, face representation and face classification, we propose a Siamese architecture especially for the representation stage and use a one-against-one support vector machine for the classification stage. LFW dataset is used for training and testing which gets a considerable result. And we also test our system on other face dataset, which has a high accuracy on the recognition.

목차

Abstract
 1. Introduction
 2. Related Work
 3. System Framework
  3.1 Image Preprocessing
  3.2 Face Representation
  3.3 Face Classification
 4. Experiments and Results
  4.1 Face Datasets
  4.2 Training on the LFW
  4.3 Results on the LFW
 5. Conclusion
 References

키워드

저자

• Weiwei Wu [ Zibo Vocational Institute in Shandong, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605