Article thumbnail

One-sample Face Recognition Using HMM Model of Fiducial Areas

By Ojo John Adedapo and Adeniran Solomon A


In most real world applications, multiple image samples of individuals are not easy to collate for recognition or verification. Therefore, there is a need to perform these tasks even if only one training sample per person is available. This paper describes an effective algorithm for recognition and verification with one sample image per class. It uses two dimensional discrete wavelet transform (2D DWT) to extract features from images; and hidden Markov model (HMM) was used for training, recognition and classification. It was tested with a subset of the AT&T database and up to 90 % correct classification (Hit) and false acceptance rate (FAR) of 0.02% was achieved

Topics: Hidden Markov Model (HMM, Recognition Rate (RR, False Acceptance Rate (FAR, Face Recognition (FR
Year: 2016
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • (external link)
  • (external link)
  • (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.