Human recognition based on ear biometric data

In the field of ear biometrics, despite recent developments, there are no freely available tools or databases captured in the wild that would ease the comparison of methods for ear biometric recognition. A new ear database captured in the wild was developed as a part of this thesis as well as a new toolbox for ear recognition. Rotation invariant local phase quantization method was also applied, for the first time in the field of ear biometrics, together with a fusion of this method with vectors of normalized images. Results are comparable but not better than the state-of-the-art, however, with the fusion we have achieved better results than the rotation invariant local phase quantization alone. The developed toolbox for ear recognition presents a new step towards standardization in the field of ear recognition and tests on the ear database have shown that the database presents greater challenge than the existing ones, but it is still comparable, which makes it suitable for further use

Human recognition based on ear biometric data

In the field of ear biometrics, despite recent developments, there are no freely available tools or databases captured in the wild that would ease the comparison of methods for ear biometric recognition. A new ear database captured in the wild was developed as a part of this thesis as well as a new toolbox for ear recognition. Rotation invariant local phase quantization method was also applied, for the first time in the field of ear biometrics, together with a fusion of this method with vectors of normalized images. Results are comparable but not better than the state-of-the-art, however, with the fusion we have achieved better results than the rotation invariant local phase quantization alone. The developed toolbox for ear recognition presents a new step towards standardization in the field of ear recognition and tests on the ear database have shown that the database presents greater challenge than the existing ones, but it is still comparable, which makes it suitable for further use

Quadratic Projection Based Feature Extraction with Its Application to Biometric Recognition

This paper presents a novel quadratic projection based feature extraction framework, where a set of quadratic matrices is learned to distinguish each class from all other classes. We formulate quadratic matrix learning (QML) as a standard semidefinite programming (SDP) problem. However, the con- ventional interior-point SDP solvers do not scale well to the problem of QML for high-dimensional data. To solve the scalability of QML, we develop an efficient algorithm, termed DualQML, based on the Lagrange duality theory, to extract nonlinear features. To evaluate the feasibility and effectiveness of the proposed framework, we conduct extensive experiments on biometric recognition. Experimental results on three representative biometric recogni- tion tasks, including face, palmprint, and ear recognition, demonstrate the superiority of the DualQML-based feature extraction algorithm compared to the current state-of-the-art algorithm

Enhancement Ear-based Biometric System Using a Modified AdaBoost Method

أن الهدف الرئيسي من هذا البحث هو تعزيز نموذج المصادقة البيومترية والتصنيف باستخدام الأذن كجزء مميز من الوجه لأنها لا تتغير بمرور الوقت ولا تتأثر بتعابير الوجه. النموذج المقترح هو سيناريو جديد لتعزيز دقة التعرف على الأذن من خلال تعديل خوارزمية تعزيز المصنف (AdaBoost) لتحسين التعلم التكيفي. للتغلب على قيود إضاءة الصورة والانسداد وسوء تسجيل الصورة نستخدم تقنية تحويل ميزة المقياس الثابت لاستخراج الميزات. تم استخدام مراحل متتالية مختلفة لتحسين دقة التصنيف. هذه المراحل هي الحصول على الصور والمعالجة المسبقة والتصفية والتنعيم واستخراج الميزات. لتقييم أداء الطريقة المقترحة، تمت مقارنة دقة التصنيف باستخدام أنواع مختلفة من المصنفات. هذه المصنفات هي Naïve Bayesian وKNN وJ48 وSVM.، خلصنا إلى أن مدى دقة التعريف لجميع قواعد البيانات التي تمت معالجتها باستخدام السيناريو المقترح يتراوح بين (٪ 93.8-٪ 97.8). تم تنفيذ النظام باستخدام MATHLAB R2017 بمعالج 2.10 جيجا هرتز و4 جيجا بايت رام.          The primary objective of this paper is to improve a biometric authentication and classification model using the ear as a distinct part of the face since it is unchanged with time and unaffected by facial expressions. The proposed model is a new scenario for enhancing ear recognition accuracy via modifying the AdaBoost algorithm to optimize adaptive learning. To overcome the limitation of image illumination, occlusion, and problems of image registration, the Scale-invariant feature transform technique was used to extract features. Various consecutive phases were used to improve classification accuracy. These phases are image acquisition, preprocessing, filtering, smoothing, and feature extraction. To assess the proposed system's performance. method, the classification accuracy has been compared using different types of classifiers. These classifiers are Naïve Bayesian, KNN, J48, and SVM. The range of the identification accuracy for all the processed databases using the proposed scenario is between (%93.8- %97.8). The system was executed using MATHLAB R2017, 2.10 GHz processor, and 4 GB RAM