Distortion Robust Biometric Recognition

abstract: Information forensics and security have come a long way in just a few years thanks to the recent advances in biometric recognition. The main challenge remains a proper design of a biometric modality that can be resilient to unconstrained conditions, such as quality distortions. This work presents a solution to face and ear recognition under unconstrained visual variations, with a main focus on recognition in the presence of blur, occlusion and additive noise distortions. First, the dissertation addresses the problem of scene variations in the presence of blur, occlusion and additive noise distortions resulting from capture, processing and transmission. Despite their excellent performance, ’deep’ methods are susceptible to visual distortions, which significantly reduce their performance. Sparse representations, on the other hand, have shown huge potential capabilities in handling problems, such as occlusion and corruption. In this work, an augmented SRC (ASRC) framework is presented to improve the performance of the Spare Representation Classifier (SRC) in the presence of blur, additive noise and block occlusion, while preserving its robustness to scene dependent variations. Different feature types are considered in the performance evaluation including image raw pixels, HoG and deep learning VGG-Face. The proposed ASRC framework is shown to outperform the conventional SRC in terms of recognition accuracy, in addition to other existing sparse-based methods and blur invariant methods at medium to high levels of distortion, when particularly used with discriminative features. In order to assess the quality of features in improving both the sparsity of the representation and the classification accuracy, a feature sparse coding and classification index (FSCCI) is proposed and used for feature ranking and selection within both the SRC and ASRC frameworks. The second part of the dissertation presents a method for unconstrained ear recognition using deep learning features. The unconstrained ear recognition is performed using transfer learning with deep neural networks (DNNs) as a feature extractor followed by a shallow classifier. Data augmentation is used to improve the recognition performance by augmenting the training dataset with image transformations. The recognition performance of the feature extraction models is compared with an ensemble of fine-tuned networks. The results show that, in the case where long training time is not desirable or a large amount of data is not available, the features from pre-trained DNNs can be used with a shallow classifier to give a comparable recognition accuracy to the fine-tuned networks.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

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

Algorithms for highly symmetric linear and integer programs

This paper deals with exploiting symmetry for solving linear and integer programming problems. Basic properties of linear representations of finite groups can be used to reduce symmetric linear programming to solving linear programs of lower dimension. Combining this approach with knowledge of the geometry of feasible integer solutions yields an algorithm for solving highly symmetric integer linear programs which only takes time which is linear in the number of constraints and quadratic in the dimensio

Algorithms for highly symmetric linear and integer programs

Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)This paper deals with exploiting symmetry for solving linear and integer programming problems. Basic properties of linear representations of finite groups can be used to reduce symmetric linear programming to solving linear programs of lower dimension. Combining this approach with knowledge of the geometry of feasible integer solutions yields an algorithm for solving highly symmetric integer linear programs which only takes time which is linear in the number of constraints and quadratic in the dimension

A comprehensive survey on Pose-Invariant Face Recognition

© 2016 ACM. The capacity to recognize faces under varied poses is a fundamental human ability that presents a unique challenge for computer vision systems. Compared to frontal face recognition, which has been intensively studied and has gradually matured in the past few decades, Pose-Invariant Face Recognition (PIFR) remains a largely unsolved problem. However, PIFR is crucial to realizing the full potential of face recognition for real-world applications, since face recognition is intrinsically a passive biometric technology for recognizing uncooperative subjects. In this article, we discuss the inherent difficulties in PIFR and present a comprehensive review of established techniques. Existing PIFR methods can be grouped into four categories, that is, pose-robust feature extraction approaches, multiview subspace learning approaches, face synthesis approaches, and hybrid approaches. The motivations, strategies, pros/cons, and performance of representative approaches are described and compared. Moreover, promising directions for future research are discussed