Comparison of compression algorithms' impact on iris recognition accuracy II: revisiting JPEG

The impact of using different lossy compression algorithms on the recognition accuracy of iris recognition systems is investigated. In particular, we consider the general purpose still image compression algorithms JPEG, JPEG2000, SPIHT, and PRVQ and assess their impact on ROC of two different iris recognition systems when applying compression to iris sample data.

[1]  Y. Fisher Fractal image compression: theory and application , 1995 .

[2]  Andreas Uhl,et al.  Comparison of Compression Algorithms' Impact on Iris Recognition Accuracy , 2007, ICB.

[3]  Andreas Uhl,et al.  Comparison of compression algorithms' impact on fingerprint and face recognition accuracy , 2007, Electronic Imaging.

[4]  Horst Bischof,et al.  Memory efficient fingerprint verification , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[5]  Luís A. Alexandre,et al.  UBIRIS: A Noisy Iris Image Database , 2005, ICIAP.

[6]  Libor Masek,et al.  MATLAB Source Code for a Biometric Identification System Based on Iris Patterns , 2003 .

[7]  Donald M. Monro,et al.  Effects of Sampling and Compression on Human IRIS Verification , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[8]  John Daugman,et al.  How iris recognition works , 2002, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  R.W. Ives,et al.  Effect of Image Compression on Iris Recognition , 2005, 2005 IEEE Instrumentationand Measurement Technology Conference Proceedings.

[10]  C. Busch,et al.  Evaluation of image compression algorithms for fingerprint and face recognition systems , 2005, Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop.

[11]  William A. Pearlman,et al.  A new, fast, and efficient image codec based on set partitioning in hierarchical trees , 1996, IEEE Trans. Circuits Syst. Video Technol..

[12]  Craig M. Arndt,et al.  Effects of compression and individual variability on face recognition performance , 2004, SPIE Defense + Commercial Sensing.

[13]  Andreas Uhl,et al.  Comparison of Lossy Image Compression Methods applied to Photorealistic and Graphical Images using P , 1998 .

[14]  Tieniu Tan,et al.  Biometric personal identification based on iris patterns , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[15]  Dexin Zhang,et al.  Efficient iris recognition by characterizing key local variations , 2004, IEEE Transactions on Image Processing.

[16]  Mislav Grgic,et al.  Effects of JPEG and JPEG2000 Compression on Face Recognition , 2005, ICAPR.

[17]  Gregory K. Wallace,et al.  The JPEG Still Image Compression Standard , 1991 .