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.
Highlights
Biometric technology has been used in the accurate determination of an individual’s identity based on physical, chemical, or behavioral attributes.[1]
The input images are transformed from raw biometric data to Zernike moments
By means of Eqs. (6) and (7), a set of descriptors are obtained. It converts the image of M × N pixel values into a pattern vector composed by the first χ TRSI Zernike moment invariants
Summary
Biometric technology has been used in the accurate determination of an individual’s identity based on physical, chemical, or behavioral attributes.[1]. The feature extraction methods like histogram of oriented gradients[16] and scale-invariant feature transform[17] are often used as descriptors of orientation, scale, and intensity for vein patterns They are not robust to noise presence and are partially invariant to translation, rotation, scale, and intensity (TRSI). Li et al.[3] use Zernike moments to describe shape features of preprocessed finger-vein images In these last works, a preprocessing stage is carried out to deal with spatial distortions and contrast changes in the input images. Each input raw biometric image is described by a pattern vector ψ1; ψ2; : : : ; ψχ obtained from the selected TRSI moment invariants.
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