Fingerprint Enhancement Based on Tensor of Wavelet Subbands for Classification

Fingerprint image enhancement is a key aspect of an automated fingerprint identification system. This paper describes an effective algorithm based on a novel lighting compensation scheme. The scheme involves the use of adaptive higher-order singular value decomposition on a tensor of wavelet subbands of a fingerprint (AHTWF) image to enhance the quality of the image. The algorithm consists of three stages. The first stage is the decomposition of an input fingerprint image of size
$2M\times 2N$
into four subbands at the first level by applying a two-dimensional discrete wavelet transform. In the second stage, we construct a tensor in
$\mathbb {R}^{M\times N\times 4}$
space. The tensor contains four wavelet subbands that serve as four frontal planes. Furthermore, the tensor is decomposed through higher-order singular value decomposition to separate the fingerprint’s wavelet subbands into detailed individual components. In the third stage, a compensated image is produced by adaptively obtaining the compensation coefficient for each frontal plane of the tensor-based on the reference Gaussian template. The experimental results indicated that the quality of the AHTWF image was higher than that of the original image. The proposed algorithm not only improves the clarity and continuity of ridge structures but also removes the background and blurred regions of a fingerprint image. Therefore, this algorithm can achieve higher fingerprint classification accuracy than related methods can.