Fusion of geometric and texture features for finger knuckle surface recognition

AbstractHand-based biometrics plays a significant role in establishing security for real-time environments involving human interaction and is found to be more successful in terms of high speed and accuracy. This paper investigates on an integrated approach for personal authentication using Finger Back Knuckle Surface (FBKS) based on two methodologies viz., Angular Geometric Analysis based Feature Extraction Method (AGFEM) and Contourlet Transform based Feature Extraction Method (CTFEM). Based on these methods, this personal authentication system simultaneously extracts shape oriented feature information and textural pattern information of FBKS for authenticating an individual. Furthermore, the proposed geometric and textural analysis methods extract feature information from both proximal phalanx and distal phalanx knuckle regions (FBKS), while the existing works of the literature concentrate only on the features of proximal phalanx knuckle region. The finger joint region found nearer to the tip of the finger is called distal phalanx region of FBKS, which is a unique feature and has greater potentiality toward identification. Extensive experiments conducted using newly created database with 5400 FBKS images and the obtained results infer that the integration of shape oriented features with texture feature information yields excellent accuracy rate of 99.12% with lowest equal error rate of 1.04%

Implementation of a Secure Internet Voting Protocol

Voting is one of the most important activities in a democratic society. In a traditional voting environment voting process sometimes becomes quite inconvenient due to the reluctance of certain voters to visit a polling booth to cast votes besides involving huge social and human resources. The development of computer networks and elaboration of cryptographic techniques facilitate the implementation of electronic voting. In this work we propose a secure electronic voting protocol that is suitable for large scale voting over the Internet. The protocol allows a voter to cast his or her ballot anonymously, by exchanging untraceable yet authentic messages. The e-voting protocol is based on blind signatures and has the properties of anonymity, mobility, efficiency, robustness, authentication, uniqueness, and universal verifiability and coercion-resistant. The proposed protocol encompasses three distinct phases - that of registration phase, voting phase and counting phase involving five parties, the voter, certification centre, authentication server, voting server and a tallying server

On the occurrence of buckler crab Cryptopodia angulata in the coastal waters of India

464-467The trend of marine non-indigenous species in India has been increasing, with more than half of the species probably being introduced by shipping. A live specimen of buckler crab Cryptopodia angulata was found along the west coast of India at 40 m depth. The recent new records at different Indian coastal locations suggest that the crab is widening its distribution. Shipping is thought to be the possible introduction vector (via ballast) for the spread of C. angulata in the coastal waters of India. Further, the favorable environmental conditions prevalent in the Indian coastal waters may facilitate the establishment and subsequent spread of C. angulata. The invasion of this buckler crab may have negative impact on the native species. Although not present in detectable numbers, C. angulata may pose a major threat to the native species, if it establishes. Information on the establishment and distribution of C. angulata from other locations along the Indian coast would be essential to comprehensively and effectively address the threat

Comparison of pharmacological inhibitors of lysine-specific demethylase 1 in glioblastoma stem cells reveals inhibitor-specific efficacy profiles

IntroductionImproved therapies for glioblastoma (GBM) are desperately needed and require preclinical evaluation in models that capture tumor heterogeneity and intrinsic resistance seen in patients. Epigenetic alterations have been well documented in GBM and lysine-specific demethylase 1 (LSD1/KDM1A) is amongst the chromatin modifiers implicated in stem cell maintenance, growth and differentiation. Pharmacological inhibition of LSD1 is clinically relevant, with numerous compounds in various phases of preclinical and clinical development, but an evaluation and comparison of LSD1 inhibitors in patient-derived GBM models is lacking.MethodsTo assess concordance between knockdown of LSD1 and inhibition of LSD1 using a prototype inhibitor in GBM, we performed RNA-seq to identify genes and biological processes associated with inhibition. Efficacy of various LSD1 inhibitors was assessed in nine patient-derived glioblastoma stem cell (GSC) lines and an orthotopic xenograft mouse model.ResultsLSD1 inhibitors had cytotoxic and selective effects regardless of GSC radiosensitivity or molecular subtype. In vivo, LSD1 inhibition via GSK-LSD1 led to a delayed reduction in tumor burden; however, tumor regrowth occurred. Comparison of GBM lines by RNA-seq was used to identify genes that may predict resistance to LSD1 inhibitors. We identified five genes that correlate with resistance to LSD1 inhibition in treatment resistant GSCs, in GSK-LSD1 treated mice, and in GBM patients with low LSD1 expression.ConclusionCollectively, the growth inhibitory effects of LSD1 inhibition across a panel of GSC models and identification of genes that may predict resistance has potential to guide future combination therapies

GPR56/ADGRG1 Inhibits Mesenchymal Differentiation and Radioresistance in Glioblastoma

A mesenchymal transition occurs both during the natural evolution of glioblastoma (GBM) and in response to therapy. Here, we report that the adhesion G-protein-coupled receptor, GPR56/ADGRG1, inhibits GBM mesenchymal differentiation and radioresistance. GPR56 is enriched in proneural and classical GBMs and is lost during their transition toward a mesenchymal subtype. GPR56 loss of function promotes mesenchymal differentiation and radioresistance of glioma initiating cells both in vitro and in vivo. Accordingly, a low GPR56-associated signature is prognostic of a poor outcome in GBM patients even within non-G-CIMP GBMs. Mechanistically, we reveal GPR56 as an inhibitor of the nuclear factor kappa B (NF-κB) signaling pathway, thereby providing the rationale by which this receptor prevents mesenchymal differentiation and radioresistance. A pan-cancer analysis suggests that GPR56 might be an inhibitor of the mesenchymal transition across multiple tumor types beyond GBM

GPR56/ADGRG1 inhibits mesenchymal differentiation and radioresistance in glioblastoma

A mesenchymal transition occurs both during the natural evolution of glioblastoma (GBM) and in response to therapy. Here, we report that the adhesion G-protein-coupled receptor, GPR56/ADGRG1, inhibits GBM mesenchymal differentiation and radioresistance. GPR56 is enriched in proneural and classical GBMs and is lost during their transition toward a mesenchymal subtype. GPR56 loss of function promotes mesenchymal differentiation and radioresistance of glioma initiating cells both in vitro and in vivo. Accordingly, a low GPR56-associated signature is prognostic of a poor outcome in GBM patients even within non-G-CIMP GBMs. Mechanistically, we reveal GPR56 as an inhibitor of the nuclear factor kappa B (NF-κB) signaling pathway, thereby providing the rationale by which this receptor prevents mesenchymal differentiation and radioresistance. A pan-cancer analysis suggests that GPR56 might be an inhibitor of the mesenchymal transition across multiple tumor types beyond GBM

Robust personal authentication using finger knuckle geometric and texture features

This paper investigates on the entire finger dorsal surface for human identity that can be extremely beneficial for forensics applications and its related fields. Further, this paper formulates a novel approach to achieve improved performance by simultaneous extraction and integration of finger knuckle geometric and texture features by score level fusion. The geometric features are derived through Angular Geometric Analysis Method (AGAM) which extracts angular-based feature information for unique identification. Similarly, Texture Feature Extraction Methods (TFEM) viz., Completed Local Ternary Pattern (CLTP) generation method, 2D Log Gabor Filter (2DLGF) method and Fourier – Scale Invariant Feature Transform (F-SIFT) method are incorporated to derive the local texture features of an acquired finger back knuckle surface. The experimental results indicate that integration of geometric and local texture features of finger knuckle regions shows decrease in error rate by 27% (in average) when compared to the existing benchmark system taken for comparison. Keywords: Finger knuckle surface, Angular geometric analysis, Completed local ternary patterns, 2D log Gabor filters, Fourier-SIFT, Phase only correlatio

Fusion of geometric and texture features for finger knuckle surface recognition

Hand-based biometrics plays a significant role in establishing security for real-time environments involving human interaction and is found to be more successful in terms of high speed and accuracy. This paper investigates on an integrated approach for personal authentication using Finger Back Knuckle Surface (FBKS) based on two methodologies viz., Angular Geometric Analysis based Feature Extraction Method (AGFEM) and Contourlet Transform based Feature Extraction Method (CTFEM). Based on these methods, this personal authentication system simultaneously extracts shape oriented feature information and textural pattern information of FBKS for authenticating an individual. Furthermore, the proposed geometric and textural analysis methods extract feature information from both proximal phalanx and distal phalanx knuckle regions (FBKS), while the existing works of the literature concentrate only on the features of proximal phalanx knuckle region. The finger joint region found nearer to the tip of the finger is called distal phalanx region of FBKS, which is a unique feature and has greater potentiality toward identification. Extensive experiments conducted using newly created database with 5400 FBKS images and the obtained results infer that the integration of shape oriented features with texture feature information yields excellent accuracy rate of 99.12% with lowest equal error rate of 1.04%

Personal recognition using finger knuckle shape oriented features and texture analysis

Finger knuckle print is considered as one of the emerging hand biometric traits due to its potentiality toward the identification of individuals. This paper contributes a new method for personal recognition using finger knuckle print based on two approaches namely, geometric and texture analyses. In the first approach, the shape oriented features of the finger knuckle print are extracted by means of angular geometric analysis and then integrated to achieve better precision rate. Whereas, the knuckle texture feature analysis is carried out by means of multi-resolution transform known as Curvelet transform. This Curvelet transform has the ability to approximate curved singularities with minimum number of Curvelet coefficients. Since, finger knuckle patterns mainly consist of lines and curves, Curvelet transform is highly suitable for its representation. Further, the Curvelet transform decomposes the finger knuckle image into Curvelet sub-bands which are termed as ‘Curvelet knuckle’. Finally, principle component analysis is applied on each Curvelet knuckle for extracting its feature vector through the covariance matrix derived from their Curvelet coefficients. Extensive experiments were conducted using PolyU database and IIT finger knuckle database. The experimental results confirm that, our proposed method shows a high recognition rate of 98.72% with lower false acceptance rate of 0.06%