Sift Algorithm for Iris Feature Extraction

Iris recognition is proving to be one of the most reliable biometric traits for personal identification In fact iris patterns have stable invariant and distinctive features for personal identification Reliable authorization and authentication are becoming necessary for many everyday applications Iris recognition has been paid more attention due to its high reliability in personal identification But iris feature extraction is easily affected by some practical factors such as inaccurate localization occlusion and nonlinear elastic deformation The objective of the study and proposed work is to adapt the increasing usage of biometric systems which can reduce the iris preprocessing and describe iris local properties effectively and have encouraging iris recognition performance This work presents an efficient algorithm of iris feature extraction based on modified scale invariant feature transform algorithm SIF

DeformIrisNet: An Identity-Preserving Model of Iris Texture Deformation

Nonlinear iris texture deformations due to pupil size variations are one of the main factors responsible for within-class variance of genuine comparison scores in iris recognition. In dominant approaches to iris recognition, the size of a ring-shaped iris region is linearly scaled to a canonical rectangle, used further in encoding and matching. However, the biological complexity of iris sphincter and dilator muscles causes the movements of iris features to be nonlinear in a function of pupil size, and not solely organized along radial paths. Alternatively to the existing theoretical models based on biomechanics of iris musculature, in this paper we propose a novel deep autoencoder-based model that can effectively learn complex movements of iris texture features directly from the data. The proposed model takes two inputs, (a) an ISO-compliant near-infrared iris image with initial pupil size, and (b) the binary mask defining the target shape of the iris. The model makes all the necessary nonlinear deformations to the iris texture to match the shape of iris in image (a) with the shape provided by the target mask (b). The identity-preservation component of the loss function helps the model in finding deformations that preserve identity and not only visual realism of generated samples. We also demonstrate two immediate applications of this model: better compensation for iris texture deformations in iris recognition algorithms, compared to linear models, and creation of generative algorithm that can aid human forensic examiners, who may need to compare iris images with large difference in pupil dilation. We offer the source codes and model weights available along with this paper

Feature Matching in Iris Recognition System using MATLAB

Iris recognition system is a secure human authentication in biometric technology. Iris recognition system consists of five stages. They are Feature matching, Feature encoding, Iris Normalization, Iris Segmentation and Image acquisition. In Image acquisition, the eye Image is captured from the CASIA database, the Image must have good quality with high resolution to process next steps. In Iris Segmentation, the Iris part is detected by using Hough transform technique and Canny Edge detection technique. Iris from an eye Image segmented. In normalization, the Iris region is converted from the circular region into a rectangular region by using polar transform technique. In feature encoding, the normalized Iris can be encoded in the form of binary bit format by using Gabor filter techniques.  In feature matching, the encoded Iris template is compared with database eye Image of Iris template and generated the matching score by using Hamming distance technique and Euclidean distance technique. Based on the matching score, we get the result. This project is developed using Image processing toolbox of Matlab software

Membrane-protein interactions in mechanosensitive channels

In this paper, we examine the mechanical role of the lipid bilayer in ion channel conformation and function with specific reference to the case of the mechanosensitive channel of large conductance (MscL). In a recent paper (Wiggins and Phillips, 2004), we argued that mechanotransduction very naturally arises from lipid-protein interactions by invoking a simple analytic model of the MscL channel and the surrounding lipid bilayer. In this paper, we focus on improving and expanding this analytic framework for studying lipid-protein interactions with special attention to MscL. Our goal is to generate simple scaling relations which can be used to provide qualitative understanding of the role of membrane mechanics in protein function and to quantitatively interpret experimental results. For the MscL channel, we find that the free energies induced by lipid-protein interaction are of the same order as the free energy differences between conductance states measured by Sukharev et al. (1999). We therefore conclude that the mechanics of the bilayer plays an essential role in determining the conformation and function of the channel. Finally, we compare the predictions of our model to experimental results from the recent investigations of the MscL channel by Perozo et al. (2002), Powl et al. (2003), Yoshimura et al. (2004), and others and suggest a suite of new experiments

Filamentation in air : evolution, control and applications

Les travaux présentés dans cette thèse portent sur la propagation non linéaire, sous forme de filaments laser, d'impulsions laser ultra-courtes dans l'atmosphère. Les résultats, principalement obtenus à partir d'expériences réalisées en laboratoire, apportent des éléments de compréhension clés en lien avec la la projection de filaments laser dans l'air. Trois aspects distincts de la filamentation sont abordés, à savoir l'évolution, le contrôle et les applications de la filamentation laser. Dans la section évolution, un filament unique a été rigoureusement caractérisé sur plusieurs dizaines de mètres. Plusieurs mesures ont été effectuées pour obtenir une image détaillée du phénomène global. En effet, la caractérisation inclut la mesure de la distribution de plasma du filament et l'évolution spectrale des impulsions laser. Également, des canaux de lumière intense, exempte d'ionisation, ont été observés et caractérisés sur plusieurs dizaines de mètres. La section sur le contrôle présente des méthodes qui pourraient éventuellement résoudre plusieurs problèmes liés à la projection de filaments puissants à longue distance. La plupart de ces méthodes se concentrent sur la fusion de filaments multiples afin d'obtenir un plus grand nombre d'électrons libres ou, un plus grand élargissement spectral. Ces méthodes comprennent l'utilisation de masques spéciaux, la diffraction d'une ouverture circulaire et un système d'optique adaptative. Enfin, la troisième partie présente deux applications prometteuses de la filamentation dans l'air. La première est la télédétection de polluants. Plusieurs cibles (gaz, cibles métalliques, nuages de fumée, aérosols, traces d'explosifs) ont été exposées à la radiation des filaments et la fluorescence caractéristique de ces cibles a été recueillie à l'aide de la technique LIDAR. Un système d'optique adaptative a été utilisé pour améliorer de façon significative les signaux de fluorescence émise. La deuxième application discutée est la génération d'impulsions dans l'infrarouge moyen via le mélange à quatre ondes durant la filamentation à deux couleurs. Le développement de nouvelles sources laser dans l'infrarouge moyen est de première importance pour résoudre des problèmes importants pour la défense et la sécurité civile. En utilisant cette méthode, des impulsions à large bande centrées entre 4-7 [Mu]m de longueur d'onde ont été produites