A simplified second-order HMM with application to face recognition

Author

Othman, H. ; Aboulnasr, T.

Author_Institution

Sch. of Inf. Technol. & Eng., Ottawa Univ., Ont., Canada

Volume

2

fYear

2001

fDate

6-9 May 2001

Firstpage

161

Abstract

In this paper, we propose a novel approach to simplify the second-order 2-D HMM as applied to the problem of Face Recognition (FR). The proposed approach exploits the nonoverlapped feature block conditions and the independence that arises in the conditional statistical relationship between feature blocks in close neighborhoods. System performance is studied and the impact of the number of states and the kernels of the state probability density function is highlighted. The system was tested on the facial database of AT&T Laboratories Cambridge [1] and a recognition rate up to 100% has been achieved with relatively low complexity

Keywords

face recognition; hidden Markov models; learning (artificial intelligence); probability; 2D HMM; AT&T Laboratories Cambridge; conditional statistical relationship; face recognition; nonoverlapped feature block; second-order HMM; state probability density function; Databases; Discrete cosine transforms; Face recognition; Hidden Markov models; Humans; Information technology; Probability density function; System performance; System testing; Viterbi algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2001. ISCAS 2001. The 2001 IEEE International Symposium on

Conference_Location

Sydney, NSW

Print_ISBN

0-7803-6685-9

Type

conf

DOI

10.1109/ISCAS.2001.921032

Filename

921032