Palmprint Recognition in Uncontrolled and Unco-operative Environment

On-line palmprint recognition and latent palmprint identification unit two branches of palmprint studies. The previous uses middle-resolution footage collected by a camera in an exceedingly} very well-controlled or contact-based surroundings with user cooperation for industrial applications and so the latter uses high resolution latent palmprints collected in crime scenes for rhetorical investigation. However, these two branches do not cowl some palmprint footage that have the potential for rhetorical investigation. Attributable to the prevalence of smartphone and shopper camera, further proof is at intervals the variability of digital footage taken in uncontrolled and uncooperative surroundings. However, their palms area unit typically noticeable. To visualize palmprint identification on footage collected in uncontrolled and uncooperative surroundings, a novel palmprint info is established Associate in nursing AN end-to-end deep learning rule is projected. The new data named NTU Palmprints from the net (NTU-PI-v1) contains 7881 footage from 2035 palms collected from the net. The projected rule consists of Associate in Nursing alignment network and a feature extraction network and is end-to-end trainable. The projected rule is compared with the progressive on-line palmprint recognition ways that and evaluated on three public contactless palmprint infos, IITD, CASIA, and PolyU and a couple of new databases, NTU-PI-v1 and NTU contactless palmprint info. The experimental results showed that the projected rule outperforms the current palmprint recognition ways that. Keywords: Biometrics, criminal and victim identification, forensics, palmprint recognition

Enhanced Palmprint Identification Using Score Level Fusion

Palm print identification has been used in various applications in several years. Various methods have been proposed for providing biometric security through palm print authentication. One such a method was feature level fusion which used multiple feature extraction and gives higher accuracy. But it needed to design a new matcher and acquired many training samples. However, it cannot adapt to scenarios like multimodal biometric, regional fusion, contactless and complete direction representation. This problem will be overcome by score level fusion method. In this article, we propose a salient and discriminative descriptor learning method (SDDLM) and gray-level co-occurrence matrix (GLCM).The score values of SDDLM and GLCM are integrated using score level fusion to provide enhanced score. Experiments were conducted on IITD palm print V1 database. The combination of SDDLM AND GLCM methods will be useful in achieving higher performance. It provides good recognition rate and reduces computation burden.

Deep-Analysis of Palmprint Representation Based on Correlation Concept for Human Biometrics Identification

The security of people requires a beefy guarantee in our society, particularly, with the spread of terrorism throughout the world. In this context, palmprint identification based on texture analysis is amongst the pattern recognition applications to recognize people. In this article, the researchers investigated a deep texture analysis for the palmprint texture pattern representation based on a fusion between several texture information extractions through multiple descriptors, such as HOG and Gabor Filters, Fractal dimensions and GLCM corresponding respectively to the frequency, model, and statistical methodologies-based texture features. They assessed the proposed deep texture analysis method as well as the applicability of the dimensionality reduction techniques and the correlation concept between the features-based fusion on the challenging PolyU, CASIA and IIT-Delhi Palmprint databases. The experimental results show that the fusion of different texture types using the correlation concept for palmprint modality identification leads to promising results.