Abstract

We introduce wavelet packet correlation filter classifiers. Correlation filters are traditionally designed in the image domain by minimization of some criterion function of the image training set. Instead, we perform classification in wavelet spaces that have training set representations that provide better solutions to the optimization problem in the filter design. We propose a pruning algorithm to find these wavelet spaces by using a correlation energy cost function, and we describe a match score fusion algorithm for applying the filters trained across the packet tree. The proposed classification algorithm is suitable for any object-recognition task. We present results by implementing a biometric recognition system that uses the NIST 24 fingerprint database, and show that applying correlation filters in the wavelet domain results in considerable improvement of the standard correlation filter algorithm.

© 2005 Optical Society of America

Digital speckle correlation method based on wavelet-packet noise-reduction processing

Xing Dai, Yan Cheong Chan, and Alex Chun Kwan So
Appl. Opt. 38(16) 3474-3482 (1999)

Biometric verification with correlation filters

B. V. K. Vijaya Kumar, Marios Savvides, Chunyan Xie, Krithika Venkataramani, Jason Thornton, and Abhijit Mahalanobis
Appl. Opt. 43(2) 391-402 (2004)

Illumination-tolerant face verification of low-bit-rate JPEG2000 wavelet images with advanced correlation filters for handheld devices

Surya Li Wijaya, Marios Savvides, and B. V. K. Vijaya Kumar
Appl. Opt. 44(5) 655-665 (2005)

Joint wavelet-transform correlator for image feature extraction

Wenlu Wang, Guofan Jin, Yingbai Yan, and Minxian Wu
Appl. Opt. 34(2) 370-376 (1995)

Extending the imaging volume for biometric iris recognition

Ramkumar Narayanswamy, Gregory E. Johnson, Paulo E. X. Silveira, and Hans B. Wach
Appl. Opt. 44(5) 701-712 (2005)