CSAW-M is a dataset that contains about 10 000 x-ray images created from mammograms. Mammograms are used to identify patients with breast cancer through a screening process with the goal of catching cancer tumours early. Modern convolutional neural networks are very sophisticated and capable of identifying patterns nearly indistinguishable to humans. CSAW-M doesn’t contain images of active cancer tumours, rather, whether the patient will develop cancer or not. Classification tasks such as this are known to require large datasets for training, which is cumbersome to acquire in the biomedical domain. In this paper we investigate how classification performance of non-trivial classification tasks scale with the size of available annotated images. To research this, a wide range of data-sets are generated from CSAW-M, with varying sample size and cancer types. Three different convolutional neural networks were trained on all data-sets. The study showed that classification performance does increase with the size of the annotated dataset. All three networks generally improved their prediction on the supplied benchmarking dataset. However, the improvements were very small and the research question could not be conclusively answered. The primary reasons for this was the challenging nature of the classification task, and the size of the data-set. Further research is required to gain more understanding of how much data is needed to yield a usable model. / CSAW-M är ett dataset som innehåller ungefär 10 000 röntgenbilder skapade från ett stort antal mammografier. Mammografi används för att identifiera patienter med bröstcancer genom en screeningprocess med målet att fånga cancerfall tidigt. Moderna konvolutionella neurala nätverk är mycket sofistikerade och kan tränas till att identifiera mönster i bilder mycket bättre än människor. CSAW-M innehåller inga bilder av cancertumörer, utan istället data på huruvida patienten kommer att utveckla cancer eller inte. Klassificeringsuppgifter som denna är kända för att kräva stora datamängder för träning, vilket är svårt att införskaffa inom den biomedicinska domänen. I denna artikel undersöker vi hur klassificerings prestanda för svåra klassificeringsuppgifter skalar med storleken på tillgänglig annoterad data. För att undersöka detta, genererades ett antal nya dataset från CSAW-M, med varierande storleksurval och cancertyp. Tre olika konvolutionella neurala nätverk tränades på alla nya data-set. Studien visar att klassificeringsprestanda ökar med storleken på den annoterade datamängden. Alla tre nätverk förbättrade generellt sin klassificeringsprestanda desto större urval som gjordes från CSAW-M. Förbättringarna var dock små och den studerade frågan kunde inte besvaras fullständigt. De främsta anledningarna till detta var klassificeringsuppgiftens utmanande karaktär och storleken på det tillgängliga datat i CSAW-M. Ytterligare forskning krävs för att få mer förståelse för hur mycket data som behövs för att skapa en användbar modell.

The use of machine learning to predict output from data, using a model, is a well studied area. There are, however, a number of real-world applications that require a model to be produced but have little or no data available of the specific environment. These situations are prominent in Intelligent Environments (IEs). The sparsity of the data can be a result of the physical nature of the implementation, such as sensors placed into disaster recovery scenarios, or where the focus of the data acquisition is on very defined user groups, in the case of disabled individuals. Standard machine learning approaches focus on a need for training data to come from the same domain. The restrictions of the physical nature of these environments can severely reduce data acquisition making it extremely costly, or in certain situations, impossible. This impedes the ability of these approaches to model the environments. It is this problem, in the area of IEs, that this thesis is focussed. To address complex and uncertain environments, humans have learnt to use previously acquired information to reason and understand their surroundings. Knowledge from different but related domains can be used to aid the ability to learn. For example, the ability to ride a road bicycle can help when acquiring the more sophisticated skills of mountain biking. This humanistic approach to learning can be used to tackle real-world problems where a-priori labelled training data is either difficult or not possible to gain. The transferral of knowledge from a related, but differing context can allow for the reuse and repurpose of known information. In this thesis, a novel composition of methods are brought together that are broadly based on a humanist approach to learning. Two concepts, Transfer Learning (TL) and Fuzzy Logic (FL) are combined in a framework, Fuzzy Transfer Learning (FuzzyTL), to address the problem of learning tasks that have no prior direct contextual knowledge. Through the use of a FL based learning method, uncertainty that is evident in dynamic environments is represented. By combining labelled data from a contextually related source task, and little or no unlabelled data from a target task, the framework is shown to be able to accomplish predictive tasks using models learned from contextually different data. The framework incorporates an additional novel five stage online adaptation process. By adapting the underlying fuzzy structure through the use of previous labelled knowledge and new unlabelled information, an increase in predictive performance is shown. The framework outlined is applied to two differing real-world IEs to demonstrate its ability to predict in uncertain and dynamic environments. Through a series of experiments, it is shown that the framework is capable of predicting output using differing contextual data.

Object category detection, the task of determining if one or more instances of a category are present in an image with their corresponding locations, is one of the fundamental problems of computer vision. The task is very challenging because of the large variations in imaged object appearance, particularly due to the changes in viewpoint, illumination and intra-class variance. Although successful solutions exist for learning object category detectors, they require massive amounts of training data. Transfer learning builds upon previously acquired knowledge and thus reduces training requirements. The objective of this work is to develop and apply novel transfer learning techniques specific to the object category detection problem. This thesis proposes methods which not only address the challenges of performing transfer learning for object category detection such as finding relevant sources for transfer, handling aspect ratio mismatches and considering the geometric relations between the features; but also enable large scale object category detection by quickly learning from considerably fewer training samples and immediate evaluation of models on web scale data with the help of part-based indexing. Several novel transfer models are introduced such as: (a) rigid transfer for transferring knowledge between similar classes, (b) deformable transfer which tolerates small structural changes by deforming the source detector while performing the transfer, and (c) part level transfer particularly for the cases where full template transfer is not possible due to aspect ratio mismatches or not having adequately similar sources. Building upon the idea of using part-level transfer, instead of performing an exhaustive sliding window search, part-based indexing is proposed for efficient evaluation of templates enabling us to obtain immediate detection results in large scale image collections. Furthermore, easier and more robust optimization methods are developed with the help of feature maps defined between proposed transfer learning formulations and the “classical” SVM formulation.

<p>Bayesian methods offer a flexible and convenient probabilistic learning framework to extract interpretable knowledge from complex and structured data. Such methods can characterize dependencies among multiple levels of hidden variables and share statistical strength across heterogeneous sources. In the first part of this dissertation, we develop two dependent variational inference methods for full posterior approximation in non-conjugate Bayesian models through hierarchical mixture- and copula-based variational proposals, respectively. The proposed methods move beyond the widely used factorized approximation to the posterior and provide generic applicability to a broad class of probabilistic models with minimal model-specific derivations. In the second part of this dissertation, we design probabilistic graphical models to accommodate multimodal data, describe dynamical behaviors and account for task heterogeneity. In particular, the sparse latent factor model is able to reveal common low-dimensional structures from high-dimensional data. We demonstrate the effectiveness of the proposed statistical learning methods on both synthetic and real-world data.</p> / Dissertation

Context. Image enhancement algorithms can be used to enhance the visual effects of images in the field of human vision. So can image enhancement algorithms be used in the field of computer vision? The convolutional neural network, as the most powerful image classifier at present, has excellent performance in the field of image recognition. This paper explores whether image enhancement algorithms can be used to improve the performance of convolutional neural networks. Objectives. The purpose of this paper is to explore the effect of image enhancement algorithms on the performance of CNN models in deep learning and transfer learning, respectively. The article selected five different image enhancement algorithms, they are the contrast limited adaptive histogram equalization (CLAHE), the successive means of the quantization transform (SMQT), the adaptive gamma correction, the wavelet transform, and the Laplace operator. Methods. In this paper, experiments are used as research methods. Three groups of experiments are designed; they respectively explore whether the enhancement of grayscale images can improve the performance of CNN in deep learning, whether the enhancement of color images can improve the performance of CNN in deep learning and whether the enhancement of RGB images can improve the performance of CNN in transfer learning?Results. In the experiment, in deep learning, when training a complete CNN model, using the Laplace operator to enhance the gray image can improve the recall rate of CNN. However, the remaining image enhancement algorithms cannot improve the performance of CNN in both grayscale image datasets and color image datasets. In addition, in transfer learning, when fine-tuning the pre-trained CNN model, using contrast limited adaptive histogram equalization (CLAHE), successive means quantization transform (SMQT), Wavelet transform, and Laplace operator will reduce the performance of CNN. Conclusions. Experiments show that in deep learning, using image enhancement algorithms may improve CNN performance when training complete CNN models, but not all image enhancement algorithms can improve CNN performance; in transfer learning, when fine-tuning the pre- trained CNN model, image enhancement algorithms may reduce the performance of CNN.

Lors de l’apprentissage d’un modèle de classification pour un nouveau domaine cible avec seulement une petite quantité d’échantillons de formation, l’application des algorithmes d’apprentissage automatiques conduit généralement à des classifieurs surdimensionnés avec de mauvaises compétences de généralisation. D’autre part, recueillir un nombre suffisant d’échantillons de formation étiquetés manuellement peut s’avérer très coûteux. Les méthodes de transfert d’apprentissage visent à résoudre ce type de problèmes en transférant des connaissances provenant d’un domaine source associé qui contient beaucoup plus de données pour faciliter la classification dans le domaine cible. Selon les différentes hypothèses sur le domaine cible et le domaine source, l’apprentissage par transfert peut être classé en trois catégories: apprentissage par transfert inductif, apprentissage par transfert transducteur (adaptation du domaine) et apprentissage par transfert non surveillé. Nous nous concentrons sur le premier qui suppose que la tâche cible et la tâche source sont différentes mais liées. Plus précisément, nous supposons que la tâche cible et la tâche source sont des tâches de classification, tandis que les catégories cible et les catégories source sont différentes mais liées. Nous proposons deux méthodes différentes pour aborder ce problème. Dans le premier travail, nous proposons une nouvelle méthode d’apprentissage par transfert discriminatif, à savoir DTL(Discriminative Transfer Learning), combinant une série d’hypothèses faites à la fois par le modèle appris avec les échantillons de cible et les modèles supplémentaires appris avec des échantillons des catégories sources. Plus précisément, nous utilisons le résidu de reconstruction creuse comme discriminant de base et améliore son pouvoir discriminatif en comparant deux résidus d’un dictionnaire positif et d’un dictionnaire négatif. Sur cette base, nous utilisons des similitudes et des dissemblances en choisissant des catégories sources positivement corrélées et négativement corrélées pour former des dictionnaires supplémentaires. Une nouvelle fonction de coût basée sur la statistique de Wilcoxon-Mann-Whitney est proposée pour choisir les dictionnaires supplémentaires avec des données non équilibrées. En outre, deux processus de Boosting parallèles sont appliqués à la fois aux distributions de données positives et négatives pour améliorer encore les performances du classificateur. Sur deux bases de données de classification d’images différentes, la DTL proposée surpasse de manière constante les autres méthodes de l’état de l’art du transfert de connaissances, tout en maintenant un temps d’exécution très efficace. Dans le deuxième travail, nous combinons le pouvoir du transport optimal (OT) et des réseaux de neurones profond (DNN) pour résoudre le problème ITL. Plus précisément, nous proposons une nouvelle méthode pour affiner conjointement un réseau de neurones avec des données source et des données cibles. En ajoutant une fonction de perte du transfert optimal (OT loss) entre les prédictions du classificateur source et cible comme une contrainte sur le classificateur source, le réseau JTLN (Joint Transfer Learning Network) proposé peut effectivement apprendre des connaissances utiles pour la classification cible à partir des données source. En outre, en utilisant différents métriques comme matrice de coût pour la fonction de perte du transfert optimal, JTLN peut intégrer différentes connaissances antérieures sur la relation entre les catégories cibles et les catégories sources. Nous avons effectué des expérimentations avec JTLN basées sur Alexnet sur les jeux de données de classification d’image et les résultats vérifient l’efficacité du JTLN proposé. A notre connaissances, ce JTLN proposé est le premier travail à aborder ITL avec des réseaux de neurones profond (DNN) tout en intégrant des connaissances antérieures sur la relation entre les catégories cible et source. / When learning a classification model for a new target domain with only a small amount of training samples, brute force application of machine learning algorithms generally leads to over-fitted classifiers with poor generalization skills. On the other hand, collecting a sufficient number of manually labeled training samples may prove very expensive. Transfer Learning methods aim to solve this kind of problems by transferring knowledge from related source domain which has much more data to help classification in the target domain. Depending on different assumptions about target domain and source domain, transfer learning can be further categorized into three categories: Inductive Transfer Learning, Transductive Transfer Learning (Domain Adaptation) and Unsupervised Transfer Learning. We focus on the first one which assumes that the target task and source task are different but related. More specifically, we assume that both target task and source task are classification tasks, while the target categories and source categories are different but related. We propose two different methods to approach this ITL problem. In the first work we propose a new discriminative transfer learning method, namely DTL, combining a series of hypotheses made by both the model learned with target training samples, and the additional models learned with source category samples. Specifically, we use the sparse reconstruction residual as a basic discriminant, and enhance its discriminative power by comparing two residuals from a positive and a negative dictionary. On this basis, we make use of similarities and dissimilarities by choosing both positively correlated and negatively correlated source categories to form additional dictionaries. A new Wilcoxon-Mann-Whitney statistic based cost function is proposed to choose the additional dictionaries with unbalanced training data. Also, two parallel boosting processes are applied to both the positive and negative data distributions to further improve classifier performance. On two different image classification databases, the proposed DTL consistently out performs other state-of-the-art transfer learning methods, while at the same time maintaining very efficient runtime. In the second work we combine the power of Optimal Transport and Deep Neural Networks to tackle the ITL problem. Specifically, we propose a novel method to jointly fine-tune a Deep Neural Network with source data and target data. By adding an Optimal Transport loss (OT loss) between source and target classifier predictions as a constraint on the source classifier, the proposed Joint Transfer Learning Network (JTLN) can effectively learn useful knowledge for target classification from source data. Furthermore, by using different kind of metric as cost matrix for the OT loss, JTLN can incorporate different prior knowledge about the relatedness between target categories and source categories. We carried out experiments with JTLN based on Alexnet on image classification datasets and the results verify the effectiveness of the proposed JTLN in comparison with standard consecutive fine-tuning. To the best of our knowledge, the proposed JTLN is the first work to tackle ITL with Deep Neural Networks while incorporating prior knowledge on relatedness between target and source categories. This Joint Transfer Learning with OT loss is general and can also be applied to other kind of Neural Networks.

Scene recognition is a hot research topic in the field of image recognition. It is necessary that we focus on the research on scene recognition, because it is helpful to the scene understanding topic, and can provide important contextual information for object recognition. The traditional approaches for scene recognition still have a lot of shortcomings. In these years, the deep learning method, which uses convolutional neural network, has got state-of-the-art results in this area. This thesis constructs a model based on multi-layer feature extraction of CNN and transfer learning for scene recognition tasks. Because scene images often contain multiple objects, there may be more useful local semantic information in the convolutional layers of the network, which may be lost in the full connected layers. Therefore, this paper improved the traditional architecture of CNN, adopted the existing improvement which enhanced the convolution layer information, and extracted it using Fisher Vector. Then this thesis introduced the idea of transfer learning, and tried to introduce the knowledge of two different fields, which are scene and object. We combined the output of these two networks to achieve better results. Finally, this thesis implemented the method using Python and PyTorch. This thesis applied the method to two famous scene datasets. the UIUC-Sports and Scene-15 datasets. Compared with traditional CNN AlexNet architecture, we improve the result from 81% to 93% in UIUC-Sports, and from 79% to 91% in Scene- 15. It shows that our method has good performance on scene recognition tasks.

Text classification using deep learning is rarely applied to tasks with more than ten target classes. This thesis investigates if deep learning can be successfully applied to a task with over 1000 target classes. A pretrained Long Short-Term Memory language model is fine-tuned and used as a base for the classifier. After five days of training, the deep learning model achieves 80.5% accuracy on a publicly available dataset, 9.3% higher than Naive Bayes. With five guesses, the model predicts the correct class 92.2% of the time.

Major advances in machine learning have made image recognition applications, with Artificial Neural Network, blossom over the recent years. The aim of this thesis was to find a solution to recognize digits from a number sequence on an ID tag, used to identify farm animals, with the help of image recognition. A Recurrent Convolutional Neural Network solution called PPNet was proposed and tested on a data set called Animal Identification Tags. A transfer learning method was also used to test if it could help PPNet generalize and better recognize digits. PPNet was then compared against Microsoft Azures own image recognition API, to determine how PPNet compares to a general solution. PPNet, while not performing as good, still managed to achieve competitive results to the Azure API.

This work presents my research about the use of Convolutional Neural Network (CNNs) for transfer learning through its application for colonic polyp classification and iris super-resolution. Traditionally, machine learning methods use the same feature space and the same distribution for training and testing the tools. Several problems in this approach can emerge as, for example, when the number of samples for training (especially in a supervised training) is limited. In the medical field, this problem is recurrent mainly because obtaining a database large enough with appropriate annotations for training is highly costly and may become impractical. Another problem relates to the distribution of textural features in a image database which may be too large such as the texture patterns of the human iris. In this case a single and specific training database might not get enough generalization to be applied to the entire domain. In this work we explore the use of texture transfer learning to surpass these problems for two applications: colonic polyp classification and iris super-resolution. The leading cause of deaths related to intestinal tract is the development of cancer cells (polyps) in its many parts. An early detection (when the cancer is still at an early stage) can reduce the risk of mortality among these patients. More specifically, colonic polyps (benign tumors or growths which arise on the inner colon surface) have a high occurrence and are known to be precursors of colon cancer development. Several studies have shown that automatic detection and classification of image regions which may contain polyps within the colon can be used to assist specialists in order to decrease the polyp miss rate. However, the classification can be a difficult task due to several factors such as the lack or excess of illumination, the blurring due to movement or water injection and the different appearances of polyps. Also, to find a robust and a global feature extractor that summarizes and represents all these pit-patterns structures in a single vector is very difficult and Deep Learning can be a good alternative to surpass these problems. One of the goals of this work is show the effectiveness of CNNs trained from scratch for colonic polyp classification besides the capability of knowledge transfer between natural images and medical images using off-the-shelf pretrained CNNs for colonic polyp classification. In this case, the CNN will project the target database samples into a vector space where the classes are more likely to be separable. The second part of this work dedicates to the transfer learning for iris super-resolution. The main goal of Super-Resolution (SR) is to produce, from one or more images, an image with a higher resolution (with more pixels) at the same time that produces a more detailed and realistic image being faithful to the low resolution image(s). Currently, most iris recognition systems require the user to present their iris for the sensor at a close distance. However, at present, there is a constant pressure to make that relaxed conditions of acquisitions in such systems could be allowed. In this work we show that the use of deep learning and transfer learning for single image super resolution applied to iris recognition can be an alternative for Iris Recognition of low resolution images. For this purpose, we explore if the nature of the images as well as if the pattern from the iris can influence the CNN transfer learning and, consequently, the results in the recognition process. / Diese Arbeit pr¨asentiert meine Forschung hinsichtlich der Verwendung von ”Transfer-Learning” (TL) in Kombination mit Convolutional Neural Networks (CNNs), um dadurch die Klassifikation von Dickdarmpolypen und die Qualit¨at von Iris Bildern (”Iris-Super-Resolution”) zu verbessern. Herk¨ommlicherweise verwenden Verfahren des maschinellen Lernens den gleichen Merkmalsraum und die gleiche Verteilung zum Trainieren und Testen der abgewendeten Methoden. Mehrere Probleme k¨onnen bei diesem Ansatz jedoch auftreten. Zum Beispiel ist es m¨ oglich, dass die Anzahl der zu trainierenden Daten (insbesondere in einem ”supervised training” Szenario) begrenzt ist. Im Speziellen im medizinischen Anwendungsfall ist man regelm¨aßig mit dem angesprochenen Problem konfrontiert, da die Zusammenstellung einer Datenbank, welche ¨ uber eine geeignete Anzahl an verwendbaren Daten verf ¨ ugt, entweder sehr kostspielig ist und/oder sich als ¨ uber die Maßen zeitaufw¨andig herausstellt. Ein anderes Problem betrifft die Verteilung von Strukturmerkmalen in einer Bilddatenbank, die zu groß sein kann, wie es im Fall der Verwendung von Texturmustern der menschlichen Iris auftritt. Dies kann zu dem Umstand f ¨ uhren, dass eine einzelne und sehr spezifische Trainingsdatenbank m¨oglicherweise nicht ausreichend verallgemeinert wird, um sie auf die gesamte betrachtete Dom¨ane anzuwenden. In dieser Arbeit wird die Verwendung von TL auf diverse Texturen untersucht, um die zuvor angesprochenen Probleme f ¨ ur zwei Anwendungen zu ¨ uberwinden: in der Klassifikation von Dickdarmpolypen und in Iris Super-Resolution. Die Hauptursache f ¨ ur Todesf¨alle im Zusammenhang mit dem Darmtrakt ist die Entwicklung von Krebszellen (Polypen) in vielen unterschiedlichen Auspr¨agungen. Eine Fr ¨uherkennung kann das Mortalit¨atsrisiko bei Patienten verringern, wenn sich der Krebs noch in einem fr ¨uhen Stadium befindet. Genauer gesagt, Dickdarmpolypen (gutartige Tumore oder Wucherungen, die an der inneren Dickdarmoberfl¨ache entstehen) haben ein hohes Vorkommen und sind bekanntermaßen Vorl¨aufer von Darmkrebsentwicklung. Mehrere Studien haben gezeigt, dass die automatische Erkennung und Klassifizierung von Bildregionen, die Polypen innerhalb des Dickdarms m¨oglicherweise enthalten, verwendet werden k¨onnen, um Spezialisten zu helfen, die Fehlerrate bei Polypen zu verringern. Die Klassifizierung kann sich jedoch aufgrund mehrerer Faktoren als eine schwierige Aufgabe herausstellen. ZumBeispiel kann das Fehlen oder ein U¨ bermaß an Beleuchtung zu starken Problemen hinsichtlich der Kontrastinformation der Bilder f ¨ uhren, wohingegen Unsch¨arfe aufgrund von Bewegung/Wassereinspritzung die Qualit¨at des Bildmaterials ebenfalls verschlechtert. Daten, welche ein unterschiedlich starkes Auftreten von Polypen repr¨asentieren, bieten auch dieM¨oglichkeit zu einer Reduktion der Klassifizierungsgenauigkeit. Weiters ist es sehr schwierig, einen robusten und vor allem globalen Feature-Extraktor zu finden, der all die notwendigen Pit-Pattern-Strukturen in einem einzigen Vektor zusammenfasst und darstellt. Um mit diesen Problemen ad¨aquat umzugehen, kann die Anwendung von CNNs eine gute Alternative bieten. Eines der Ziele dieser Arbeit ist es, die Wirksamkeit von CNNs, die von Grund auf f ¨ ur die Klassifikation von Dickdarmpolypen konstruiert wurden, zu zeigen. Des Weiteren soll die Anwendung von TL unter der Verwendung vorgefertigter CNNs f ¨ ur die Klassifikation von Dickdarmpolypen untersucht werden. Hierbei wird zus¨atzliche Information von nichtmedizinischen Bildern hinzugezogen und mit den verwendeten medizinischen Daten verbunden: Information wird also transferiert - TL entsteht. Auch in diesem Fall projiziert das CNN iii die Zieldatenbank (die Polypenbilder) in einen vorher trainierten Vektorraum, in dem die zu separierenden Klassen dann eher trennbar sind, daWissen aus den nicht-medizinischen Bildern einfließt. Der zweite Teil dieser Arbeit widmet sich dem TL hinsichtlich der Verbesserung der Bildqualit¨at von Iris Bilder - ”Iris- Super-Resolution”. Das Hauptziel von Super-Resolution (SR) ist es, aus einem oder mehreren Bildern gleichzeitig ein Bild mit einer h¨oheren Aufl¨osung (mit mehr Pixeln) zu erzeugen, welches dadurch zu einem detaillierteren und somit realistischeren Bild wird, wobei der visuelle Bildinhalt unver¨andert bleibt. Gegenw¨artig fordern die meisten Iris- Erkennungssysteme, dass der Benutzer seine Iris f ¨ ur den Sensor in geringer Entfernung pr¨asentiert. Jedoch ist es ein Anliegen der Industrie die bisher notwendigen Bedingungen - kurzer Abstand zwischen Sensor und Iris, sowie Verwendung von sehr teuren hochqualitativen Sensoren - zu ver¨andern. Diese Ver¨anderung betrifft einerseits die Verwendung von billigeren Sensoren und andererseits die Vergr¨oßerung des Abstandes zwischen Iris und Sensor. Beide Anpassungen f ¨ uhren zu Reduktion der Bildqualit¨at, was sich direkt auf die Erkennungsgenauigkeit der aktuell verwendeten Iris- erkennungssysteme auswirkt. In dieser Arbeit zeigen wir, dass die Verwendung von CNNs und TL f ¨ ur die ”Single Image Super-Resolution”, die bei der Iriserkennung angewendet wird, eine Alternative f ¨ ur die Iriserkennung von Bildern mit niedriger Aufl¨osung sein kann. Zu diesem Zweck untersuchen wir, ob die Art der Bilder sowie das Muster der Iris das CNN-TL beeinflusst und folglich die Ergebnisse im Erkennungsprozess ver¨andern kann.