Balanced uncertainty wavelets for fingerprint compression

We present biorthogonal and orthonormal wavelets for embedded zerotree wavelet compression of fingerprint images. By simulated annealing over the wavelet filter coefficients, using a composite cost function dependent upon a parameter k/sup 2/ which weights the relative importance of the wavelet's bandwidth and time dispersion, a series of wavelets is obtained, each of which is optimal in terms of a particular Heisenberg uncertainty 'footprint', i.e. a particular trade-off between bandwidth and time dispersion. The psychovisually optimal wavelet for fingerprint image compression is determined by fingerprint experts by examination of images compressed and recovered using the series of wavelets. Psychovisually tuned wavelets were found to yield superior visual fidelity to standard wavelets and also to wavelets optimized for minimum rms error.