The impact of collarette region-based convolutional neural network for iris recognition

Iris recognition is a biometric technique that reliably and quickly recognizes a person by their iris based on unique biological characteristics. Iris has an exceptional structure and it provides very rich feature spaces as freckles, stripes, coronas, zigzag collarette area, etc. It has many features where its growing interest in biometric recognition lies. This paper proposes an improved iris recognition method for person identification based on Convolutional Neural Networks (CNN) with an improved recognition rate based on a contribution on zigzag collarette area - the area surrounding the pupil - recognition. Our work is in the field of biometrics especially iris recognition; the iris recognition rate using the full circle of the zigzag collarette was compared with the detection rate using the lower semicircle of the zigzag collarette. The classification of the collarette is based on the Alex-Net model to learn this feature, the use of the couple (collarette/CNN) allows for noiseless and more targeted characterization and also an automatic extraction of the lower semicircle of the collarette region, finally, the SVM training model is used for classification using grayscale eye image data taken from (CASIA-iris-V4) database. The experimental results show that our contribution proves to be the best accurate, because the CNN can effectively extract the image features with higher classification accuracy and because our new method, which uses the lower semicircle of the collarette region, achieved the highest recognition accuracy compared with the old methods that use the full circle of collarette region

On Some Existence and Uniqueness Results for a Class of Equations of Order 0

This paper investigates the existence and uniqueness of solution for a class of nonlinear fractional differential equations of fractional order 0<α≤1 in arbitrary time scales. The results are established using extensions of Krasnoselskii-Krein, Rogers, and Kooi conditions

Toward a Full Integration of the Arabic Language into ‘Intel ACAT’ Assitive Platform

Background:Assisting people with severe physical limitations with information technology has been an active area of research in recent years. Many researchers’ efforts are built on assistive devices which are often used to offset the impact of the resulted physical impairments.The Assistive Context-Aware Toolkit (ACAT) is the widely known project in this area. After being released as open source, the developers’ community helped to integrate many languages such as French and Spanich. However, many languages are still messing and to the best of our knowledge, the Arabic-speaking users still can not use the platform as no significant effort to integrate the Arabic language have been previously undertaken.Methods:This paper firstly, provides an overview on ACAT; the specifically-developed platform by Intel Labs for Dr. Stephen Hawking. Besides, it describes the ways in which ACAT may be used to enhance the capacity to take part in fundamental and instrumental activities of every day living and upgrade one's autonomy in general.Secondly, we outline our contributions in integrating the Arabic language into the keyboard, the intelligent predictive text engine and all interfaces of this unique and highly configurable system. Results:Our integration evolved after resolving many issues and we succeeded in integrating the Arabic language in interfaces, keyboard and word prediction engine. Most of other ACAT features (Facial gesture recognition, Mouse Navigation., etc) are functional. Conclusion:This work is a step forward to make the intel ACAT platform completely available in Arabic language. Therefore, Arabic-speaking patient can now get the benefits from this platform and are able to perform common tasks such as documents editing and management, Web surfing, writing emails and above all, communicating with others easily.The Arabic Text-to-speech engine integration is planned for future works

On the Existence and Uniqueness for High Order Fuzzy Fractional Differential Equations with Uncertainty

A class fuzzy fractional differential equation (FFDE) involving Riemann-Liouville H-differentiability of arbitrary order q>1 is considered. Using Krasnoselskii-Krein type conditions, Kooi type conditions, and Rogers conditions we establish the uniqueness and existence of the solution after determining the equivalent integral form of the solution