Impact of the lossy image compression on the biometric system accuracy: a case study of hand biometrics

Biometric recognition systems are used in several cases, to recognise people using images. Storing of large images require large storage space. To reduce the storage space, compression methods are employed. In this paper, we analyse the effect of lossy image compression on the performance of biometric identification systems. We propose a scheme to evaluate the recognition performance at low bitrates of hand images. The images are compressed using set partitioning in hierarchical trees (SPIHT) encoding. A powerful feature extraction algorithm based on quantising the phase information of the local Fourier transform is used. The nearest neighbour (NN) classifier and the support vector machine (SVM) classifier are employed to classify the feature extraction. The obtained results show at the compression does not significantly affect the performance of recognition operation at low bitrate for unimodal and multimodal systems. Thus, the low bitrate images perform equivalent to uncompressed images in the recognition system.