Fast extraction of multi-resolution Gabor features

Gabor filter responses are general purpose features for computer vision and image processing and have been very successful in many application areas, for example in bio- metric authentication (fingerprint matching, face detection, face recognition and iris recognition). In a typical feature construction, filters are utilised as a multi-resolution structure of several filters tuned to different frequencies and orientations. The multi-resolution structure is similar to wavelets, but the non-orthogonality of Gabor functions implies the main weakness: computational heaviness. The high computational complexity prevents their use in many real-time or near real-time tasks. In this study, an efficient sequential computation method for multi-resolution Gabor features is presented.

[1]  Y. L. Tong.  The multivariate normal distribution , 1989 .

[2]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

[3]  Jiri Matas,et al.  Feature-based affine-invariant localization of faces , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Joni-Kristian Kämäräinen,et al.  Invariance properties of Gabor filter-based features-overview and applications , 2006, IEEE Transactions on Image Processing.

[5]  Yi Chen,et al.  Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features , 2007 .

[6]  Oscar Nestares,et al.  Efficient spatial-domain implementation of a multiscale image representation based on Gabor functions , 1998, J. Electronic Imaging.

[7]  Thomas Serre,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  N. Ranganathan,et al.  Efficient computation of gabor filter based multiresolution responses , 1994, Pattern Recognit..

[9]  Miroslav Hamouz.  Feature-based affine-invariant detection and localization of faces , 2004 .

[10]  John Daugman,et al.  Probing the Uniqueness and Randomness of IrisCodes: Results From 200 Billion Iris Pair Comparisons , 2006, Proceedings of the IEEE.

[11]  Josef Kittler,et al.  Object Evidence Extraction Using Simple Gabor Features and Statistical Ranking , 2005, SCIA.

[12]  LinLin Shen,et al.  Face authentication test on the BANCA database , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[13]  Nagarajan Ranganathan,et al.  An architecture to implement multiresolution , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[14]  Joni-Kristian Kämäräinen,et al.  Fundamental frequency Gabor filters for object recognition , 2002, Object recognition supported by user interaction for service robots.

[15]  Joni-Kristian Kämäräinen,et al.  Simple Gabor feature space for invariant object recognition , 2004, Pattern Recognit. Lett..