Comparison of compression algorithms' impact on fingerprint and face recognition accuracy

The impact of using dierent lossy compression algorithms on the matching accuracy of fingerprint and face recognition systems is investigated. In particular, we relate rate-distortion performance as measured in PSNR to the matching scores as obtained by the recognition systems. JPEG2000 and SPIHT are correctly predicted by PSNR to be the most suited compression algorithms to be used in fingerprint and face recognition systems. Fractal compression is identified to be least suited for the use in the investigated recognition systems, although PSNR suggests JPEG to deliver worse recognition results in the case of face imagery. JPEG compression performs surprisingly well at high bitrates in face recognition systems, given the low PSNR performance observed. With the increasing usage of biometric systems the question arises naturally how to store and handle the acquired sensor data. In this context, the compression of these data may become imperative under certain circumstances due to the large amounts of data involved. Among other possibilities (e.g. like template storage on IC cards), compression technology may be used in two stages of the processing chain in classical biometric recognition: 1. Storage of reference data: In most template databases (where the reference data of the enrolled in- dividuals is stored) only the extracted features required for the matching step are stored as opposed to retaining the originally acquired sensor data. However, in case the features should be replaced for some reason (e.g. when a superior or licence-free matching technique involving a dierent feature set becomes available), having stored only extracted features implies the requirement for all legitimate users for a re- enrollment, which can be expensive and is highly undesired since user-acceptance of the entire biometric system will suer.