Zernike moment invariants for hand vein pattern description from raw biometric data

Abstract

We propose an invariant description method based on Zernike moments to classify hand vein patterns from raw infrared (IR) images. Orthogonal moments provide linearly independent descriptors and are invariant to affine transformations, such as translation, rotation, and scaling. A mathematical expression is given to derive a set of moment invariants. The obtained features have all the properties of moment invariants with the additional feature of image contrast invariance. For dorsal hand vein pattern acquisition, an IR imaging system is implemented. Also, a public database is used for a palm vein recognition task. A correct rate classification (CRC) above 99.9% is achieved using a set of rotation, scale, and intensity Zernike moment invariants. Additionally, multilayer perceptron and K-nearest neighbors are used as classifiers having as input data the Zernike normalized moments. A discriminative feature evaluation of the image moments allows the reduction of the number of descriptors while maintaining a high classification rate of 99%. The efficiency of the moment descriptors is evaluated in terms of accuracy and reduced computational cost by (a) avoiding the necessity of a preprocessing stage and (b) reducing the feature vector dimension. Experimental results show that Zernike moment invariants are able to achieve hand vein recognition without image preprocessing or image normalization with respect to change of size, rotation, and intensity.

Vein pattern classification using convolutional neuronal network and moment invariants

“…A set of Translation, Rotation, Scale and Intensity (TRSI) Zernike moment invariants are given by, 27 ψl,l (f…”

Secure Palmprint Recognition based on Multispectral Sequential Capture

“…Another category that is also used, this category relies on statistics-based approaches; they have shown good performance [28][29]. A multitude of statistical methods have been employed in this category such as variance, standard deviation, energy and histograms of local binary models [30], Zernike and Hu moments [31]. Some transformations have also been used such as the wavelet transform to convert the palmprint image into a small number of wavelet coefficients, and then calculate the variance and the mean of these coefficients to generate the image characteristics [32].…”