Learning a Convolutional Neural Network with Additional Information

Department of Electrical EngineeringLearning representations for object recognition and image quality enhancement with the deep convolutional neural network approach has received a great deal of attention in the past several years and recently has gained widespread popularity in the field of computer vision and image processing. Many convolutional neural networks based researches has shown successful results in image processing and computer vision area by feeding only raw images into the convolutional neural network model to learn network parameters. In supervised learning, every input image in our traingset is "a question" and corresponding label or groundtruth is "a correct answer" that we would have quite liked the algorithms have predicted on that image. Even though the CNN models can be skillful enough to solve a problem by learning a large trainingset, we expect that if CNNs were feed with additional information, as well as with images, that would leads learning in a better way, it may perform better. It is like giving helpful hints when teaching a child. This thesis presents fingerprint liveness detection and space-varying deblur methods based on CNN. The first fingerprint liveness detection belongs to a classification problem and the second space-varying deblur belongs to a prediction problem of original sharpen image. Instead of training CNNs with input images only, we provide additional information with which CNNs can learn features in a domain specific, or a problem specific way. Simple additional information that can obtain fairly easily but crucial for a given task is used as an additional input of each CNN. Sweat pore map is used as additional information for fingerprint liveness detection and spatial pixel indices are used as additional information for space-varying deblur. For fingerprint liveness detection, the sweat pore map provides enough hint to allow CNN to learn features for specific regions such as regions right at the pores, regions around the pores, regions in the ridges that do not contain pores. Our fingerprint liveness detection method outperforms the best algorithms of fingerprint liveness detection competition 2013(LivDet2013). For CNN based space-varying debur, the spatial pixel indices provide enough hint to allow CNN to learn filters that have stronger response at specific areas of an image. Our non-stationary lens blur method is the first CNN model that directly outputs the restored image from a blurry image, without any assumption or approximation of block-wise spatially invariant blur. The proposed deblurring method provides the state-of-the-art performances. We established procedures for building training sets from real-world lenses and cameras for restoration of lens blur.ope

Roadmap on signal processing for next generation measurement systems

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System

Handbook of Vascular Biometrics

This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers

Fingerabdruckswachstumvorhersage, Bildvorverarbeitung und Multi-level Judgment Aggregation

Im ersten Teil dieser Arbeit wird Fingerwachstum untersucht und eine Methode zur Vorhersage von Wachstum wird vorgestellt. Die Effektivität dieser Methode wird mittels mehrerer Tests validiert. Vorverarbeitung von Fingerabdrucksbildern wird im zweiten Teil behandelt und neue Methoden zur Schätzung des Orientierungsfelds und der Ridge-Frequenz sowie zur Bildverbesserung werden vorgestellt: Die Line Sensor Methode zur Orientierungsfeldschätzung, gebogene Regionen zur Ridge-Frequenz-Schätzung und gebogene Gabor Filter zur Bildverbesserung. Multi-level Jugdment Aggregation wird eingeführt als Design Prinzip zur Kombination mehrerer Methoden auf mehreren Verarbeitungsstufen. Schließlich wird Score Neubewertung vorgestellt, um Informationen aus der Vorverarbeitung mit in die Score Bildung einzubeziehen. Anhand eines Anwendungsbeispiels wird die Wirksamkeit dieses Ansatzes auf den verfügbaren FVC-Datenbanken gezeigt.Finger growth is studied in the first part of the thesis and a method for growth prediction is presented. The effectiveness of the method is validated in several tests. Fingerprint image preprocessing is discussed in the second part and novel methods for orientation field estimation, ridge frequency estimation and image enhancement are proposed: the line sensor method for orientation estimation provides more robustness to noise than state of the art methods. Curved regions are proposed for improving the ridge frequency estimation and curved Gabor filters for image enhancement. The notion of multi-level judgment aggregation is introduced as a design principle for combining different methods at all levels of fingerprint image processing. Lastly, score revaluation is proposed for incorporating information obtained during preprocessing into the score, and thus amending the quality of the similarity measure at the final stage. A sample application combines all proposed methods of the second part and demonstrates the validity of the approach by achieving massive verification performance improvements in comparison to state of the art software on all available databases of the fingerprint verification competitions (FVC)