The algorithm studies of hidden Markov model in face distinguishing

  • Authors:

  • Han Quanli;Shi Zengfang

  • Affiliations:

  • Department of Mechanical and Electronic Engineering, Henan Polytechnic Institute, Nan yang, China;Department of Mechanical and Electronic Engineering, Henan Polytechnic Institute, Nan yang, China

  • Venue:
  • CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 3
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

Hidden Markov Models(HMM) have been successfully used in speech recognition where data is essentially one-dimensional. An new approach is proposed. In this approach, Dauechies orthogonal wavelet transform is used to preprocess the original face image, resulting in its four sub-images belonging to different frequency bands, and the sub-images are used to learning and recognition based on HMM. A algorithm is designed to combine the multiple sort results, and the Karhunen Loeve Transform (KL T) was used to extract a set of observations that improving the method by Asmaria. This approach increases the ratio of recognition and reduces the time of computing. The experimentations prove the approach is rational.