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The Effect of Training Data Selection on Face Recognition in Surveillance Application

  • Jamal Ahmad Dargham
  • Ali Chekima
  • Ervin Gubin Moung
  • Segiru Omatu
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 373)

Abstract

Face recognition is an important biometric method because of its potential applications in many fields, such as access control and surveillance. In surveillance applications, the distance between the subject and the camera is changing. Thus, in this paper, the effect of the distance between the subject and the camera, distance class, the effect of the number of images per class, and also the effect of database used for training have been investigated. The images in the database were equally divided into three classes: CLOSE, MEDIUM, and FAR, according to the distance of the subject from the camera. It was found that using images from the FAR class for training gives better performance than using either the MEDIUM or the CLOSE class. In addition, it was also found that using one image from each class for training gives the same recognition performance as using three images from the FAR class for training. It was also found that as the number of images per class increases, the recognition performance also increases. Lastly, it was found that by using one image per class from all the available database sessions gives the best recognition performance.

Keywords

Principal Component Analysis PCA training data selection face recognition surveillance distance from the camera 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jamal Ahmad Dargham
    • 1
  • Ali Chekima
    • 1
  • Ervin Gubin Moung
    • 1
  • Segiru Omatu
    • 2
  1. 1.Fakulti KejuruteraanUniversiti Malaysia Sabah, Jalan UMSKota KinabaluMalaysia
  2. 2.Faculty of Engineering, Department of Electronics, Information and Communication EngineeringOsaka Institute of TechnologyAsahi-kuJapan

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