In this paper, a multi-resolution feature extraction algorithm for palm-print recognition is proposed based on two-dimensional discrete wavelet transform (2D-DWT), which efficiently exploits the local spatial variations in a palmprint image. The entire image is segmented into several small spatial

Abstract. This paper aims at the deficiency of classical PCA algorithm on only extracting linear palm print features, sufficiently uses the double matching thought of recognition technology to propose a new algorithm which relatively efficient in extracting the classified identification information

A successful biometric system depends on the accuracy with which it works. The degree of accuracy is measured with biometric parameters. This paper addresses the various parameters used in the analysis and measurement of a Biometric system. This paper also presents experimental results of a palm

This paper proposes an automated system for recognizing palmprints for biometric identification of individuals. Complex Zernike moments are constructed using a set of complex polynomials which form a complete orthogonal basis set defined on the unit disc. Palmprint images are projected onto the basis set resulting in a set of complex signals. The magnitude of the complex value is computed and a scalar value is derived from it by computing the mean of the vector elements. Classification is done by subtracting the test samples from the mean of the training set. The data set consists of 80 images divided into 4 classes. Accuracy obtained is comparable to the best results reported in literature

Abstract-- In this paper, a spectral feature extraction algorithm is proposed for palm-print recognition, which can efficiently capture the detail spatial variations in a palm-print image. The entire image is segmented into several spatial modules and the task of feature extraction is carried out

In this paper, a spectral feature extraction algorithm is proposed for palm-print recognition, which can efficiently capture the detail spatial variations in a palm-print image. The entire image is segmented into several spatial modules and the task of feature extraction is carried out using two

Abstractó In this paper, a novel pre-processing algorithm is introduced to identify the principal lines from a palm-print image and a discrete cosine transform (DCT) domain feature extraction algorithm is then employed for palm-print recognition, which can efficiently capture the spatial variations

Palm print recognition is a proliferating technique in the ever increasing use of Biometrics for security and identification. Our paper proposes a technique for palm print recognition using the Discrete Cosine Transform, Haar Transform and the DCT Wavelet along with tests related

Now-a-Days Biometric based recognition is the most popular human recognition pattern. Biometric based recognition is an approach using the biological features inherent in each individual. They are processed based on the identical, portable and arduous duplicate characteristics. In the Palmprint

of spectrum images are aligned and then used to extract palmprint features using Contourlet Transform CT. It is a multiresolution and multidirection transform which can be effective in capturing the palm features. Finally, Genetic Algorithm is used for feature selections in order to have high performance