Fingerprint image compression standard based on wave atoms decomposition and self organizing feature map

Abstract

Multiresolution analysis tools have been successfully applied to fingerprint image compression for more than a decade. Fingerprint image compression and reconstruction standards used by the US Federal Bureau of Investigation (FBI) are based upon the popular 9/7 discrete wavelet transform (DWT). A novel fingerprint image compression technique based on recently proposed multiresolution analysis tool i.e. wave atoms decomposition is proposed. Wave atoms decomposition has been specifically designed for enhanced representation of oscillatory patterns and to convey temporal and spatial information more precisely. Our proposed compression scheme is based upon vector quantization of decomposed wave atoms representation of fingerprint images using self organizing feature map (SOM). The quantized information is later encoded using arithmetic entropy scheme. The proposed compression scheme outperforms the FBI fingerprint image compression standard, the wavelet scalar quantization (WSQ). Data mining, law enforcement, border security, and forensic applications can potentially benefit from our proposed compression technique.