Recognition of Palmprint using Eigenpalms
نویسندگان
چکیده
This paper presents a new method for personal recognition using palmprints. This method uses inkless, normalized palmprint images to generate eigenpalms. Every palmprint image is thus characterized by a feature vector, consisting of weights from eigenpalm images. The performance of proposed method using two measures, i.e., minimum Euclidean distance and maximum similarity measure, is evaluated. The low-resolution images of 100 dpi, which dominantly capture the palmprint creases, have been used in the experiments. The experimental results show that this method achieves high recognition rate when the similarity criterion is used to recognize 300 inkless palmprint test images.
منابع مشابه
Palmprint recognition using eigenpalms features
In this paper, we propose a palmprint recognition method based on eigenspace technology. By means of the Karhunen–Loeve transform, the original palmprint images are transformed into a small set of feature space, called ‘‘eigenpalms’’, which are the eigenvectors of the training set and can represent the principle components of the palmprints quite well. Then, the eigenpalm features are extracted...
متن کاملA Novel Approach to Eigenpalm Features Using Feature-Partitioning Framework
Eigenpalms, a well-known approach, extracts features from palmprint images using conventional PCA technique. However eigenpalms does not exploit neighbourhood (local) information due to its vector representation of palmprint images. In our work here, we propose a feature-partitioning framework that uses a more efficient and appropriate matrix representation of images. Our novel feature partitio...
متن کاملRecognition of Palmprints using Eigenpalm
This paper presents a new method for personal recognition using palmprints. This method uses, inkless, normalized palmprint images to generate eigenpalms. Every palmprint image is thus characterized by a feature vector, consisting of weights from eigenpalm images. The performance of proposed method using two measures i.e., minimum Euclidean distance and maximum similarity measure, is evaluated....
متن کاملAnalysis of performance of palmprint matching with enforced sparsity
a r t i c l e i n f o a b s t r a c t In this paper, a new and simple palmprint recognition solution based on sparse representation is suggested. It is shown that when the aim is to recover a palmprint from a limited number of observations as a linear combination of measurements of the same palmprint class, the ensuing representation in intrinsically very sparse. It can be efficiently computed ...
متن کاملLocal Derivative Pattern with Smart Thresholding: Local Composition Derivative Pattern for Palmprint Matching
Palmprint recognition is a new biometrics system based on physiological characteristics of the palmprint, which includes rich, stable, and unique features such as lines, points, and texture. Texture is one of the most important features extracted from low resolution images. In this paper, a new local descriptor, Local Composition Derivative Pattern (LCDP) is proposed to extract smartly stronger...
متن کامل