An efficient fingerprint image compression technique based on wave atoms decomposition and multistage vector quantization

Abstract

Modern fingerprint image compression and reconstruction standards used by the US Federal Bureau of Investigation (FBI) are based upon the popular biorthogonal 9-7 discrete wavelet transform. Multiresolution analysis tools have been successfully applied to fingerprint image compression for more than a decade; we propose a novel fingerprint image compression technique based on wave atoms decomposition and multistage vector quantization. Wave atoms decomposition has been specifically designed for enhanced representation of oscillatory patterns and to convey precise temporal and spatial information. Our proposed compression scheme is based upon multistage vector quantization of processed wave atoms representation of fingerprint images. Wave atoms expansion is processed using mathematical morphological operators to emphasize and retain significant coefficients for transmission. Quantized information is encoded using arithmetic entropy scheme. The proposed image compression standard outperforms other well established methods and achieves PSNR gain up to 8.07 dB in comparison to FBI's wavelet scalar quantization. Data mining, law enforcement, border security, and forensic applications can potentially benefit from our compression scheme.