Dual iris authentication system using dezert smarandache theory

In this paper, a dual iris authentication using Dezert Smarandache theory is presented. The proposed method consists of three main steps: In the first one, the iris images are segmented in order to extract only half iris disc that contains relevant information and is less affected by noise. For that, a Hough transform is used. The segmented images are normalized by Daugman rubber sheet model. In the second step, the normalized images are analyzed by a bench of two 1D Log-Gabor filters to extract the texture characteristics. The encoding is realized with a phase of quantization developed by J. Daugman to generate the binary iris template. For the authentication and the similarity measurement between both binary irises templates, the hamming distances are used with a previously calculated threshold. The score fusion is applied using DSmC combination rule. The proposed method has been tested on a subset of iris database CASIA-IrisV3-Interval. The obtained results give a satisfactory performance with accuracy of 99.96%, FAR of 0%, FRR of 3.89%, EER of 2% and processing time for one iris image of 12.36 s

On Generative Adversarial Network Based Synthetic Iris Presentation Attack And Its Detection

Human iris is considered a reliable and accurate modality for biometric recognition due to its unique texture information. Reliability and accuracy of iris biometric modality have prompted its large-scale deployment for critical applications such as border control and national identification projects. The extensive growth of iris recognition systems has raised apprehensions about the susceptibility of these systems to various presentation attacks. In this thesis, a novel iris presentation attack using deep learning based synthetically generated iris images is presented. Utilizing the generative capability of deep convolutional generative adversarial networks and iris quality metrics, a new framework, named as iDCGAN is proposed for creating realistic appearing synthetic iris images. In-depth analysis is performed using quality score distributions of real and synthetically generated iris images to understand the effectiveness of the proposed approach. We also demonstrate that synthetically generated iris images can be used to attack existing iris recognition systems. As synthetically generated iris images can be effectively deployed in iris presentation attacks, it is important to develop accurate iris presentation attack detection algorithms which can distinguish such synthetic iris images from real iris images. For this purpose, a novel structural and textural feature-based iris presentation attack detection framework (DESIST) is proposed. The key emphasis of DESIST is on developing a unified framework for detecting a medley of iris presentation attacks, including synthetic iris. Experimental evaluations showcase the efficacy of the proposed DESIST framework in detecting synthetic iris presentation attacks

Clasificación automática de la calidad de los arándanos usando imágenes de espectro swir con deep learning

Tesis (Ingeniero Civil Informático)El análisis empírico de la calidad de los arándanos en la etapa de post-cosecha presenta problemas reflejados en la calidad del producto empaquetado. Aprovechando los beneficios de utilizar el espectro SWIR se capturan imágenes de arándanos de buena y mala calidad, para luego realizar un diseño de base de datos de arándanos con 36.469 imágenes de arándanos de buena calidad y 60.615 arándanos de mala calidad, sumando un total de 97.044 imágenes. Con estas imágenes se realizan cuatro rondas de experimentos principales, dónde las primeras dos tienen un enfoque en el análisis de la intensidad de los píxeles y las otras dos analizan la textura de las imágenes, utilizando dos redes neuronales convolucionales, LeNet y Smaller VGG. El análisis de las texturas (filtro BSIF 3x3) presenta un mejor comportamiento en Smaller VGG, dónde el mejor resultado registrado se alcanza con un 96,34% de train y un 94,05 de test, manteniendo un análisis en el fruto reflejado en un mapa de calor