A Hybrid Multibiometric System for Personal Identification Based on Face and Iris Traits. The Development of an automated computer system for the identification of humans by integrating facial and iris features using Localization, Feature Extraction, Handcrafted and Deep learning Techniques.

Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.Higher Committee for Education Development in Ira

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

Learning Multimodal Structures in Computer Vision

A phenomenon or event can be received from various kinds of detectors or under different conditions. Each such acquisition framework is a modality of the phenomenon. Due to the relation between the modalities of multimodal phenomena, a single modality cannot fully describe the event of interest. Since several modalities report on the same event introduces new challenges comparing to the case of exploiting each modality separately. We are interested in designing new algorithmic tools to apply sensor fusion techniques in the particular signal representation of sparse coding which is a favorite methodology in signal processing, machine learning and statistics to represent data. This coding scheme is based on a machine learning technique and has been demonstrated to be capable of representing many modalities like natural images. We will consider situations where we are not only interested in support of the model to be sparse, but also to reflect a-priorily known knowledge about the application in hand. Our goal is to extract a discriminative representation of the multimodal data that leads to easily finding its essential characteristics in the subsequent analysis step, e.g., regression and classification. To be more precise, sparse coding is about representing signals as linear combinations of a small number of bases from a dictionary. The idea is to learn a dictionary that encodes intrinsic properties of the multimodal data in a decomposition coefficient vector that is favorable towards the maximal discriminatory power. We carefully design a multimodal representation framework to learn discriminative feature representations by fully exploiting, the modality-shared which is the information shared by various modalities, and modality-specific which is the information content of each modality individually. Plus, it automatically learns the weights for various feature components in a data-driven scheme. In other words, the physical interpretation of our learning framework is to fully exploit the correlated characteristics of the available modalities, while at the same time leverage the modality-specific character of each modality and change their corresponding weights for different parts of the feature in recognition

(Automatic) detection, poses determination and identification of horses with methods of 2D and 3D image processing using biometrics

Die Identifikation von Individuen beim Menschen anhand von Gesichtsaufnahmen wurde bereits in zahlreichen Arbeiten behandelt. Dagegen umfasst die Literatur nur wenige Arbeiten, welche entsprechende Ansätze der Gesichtserkennung beim Menschen zur Unterscheidung von Nutztieren untersuchen. Zudem konnten keine Arbeiten ausgemacht werden, die eine Detektion der Köpfe von Tieren (Nutztiere wie Rind, Pferd und Schaf) mit seitlich am Kopf ausgerichteten Augen behandeln. Ein entsprechendes System zur Identifikation von Nutztieren anhand ihrer Gesichter per Kamera stellt eine Alternative zur Erkennung durch am Tier befestigte RFID-Transponder zum Monitoring der Tiere in der Präzisionstierhaltung (PLF) dar. Das Hauptziel der vorliegenden Arbeit war die Entwicklung und Anwendung von Methoden, die es weitestgehend automatisch ermöglichen Individuen bei Pferden anhand von Kameradaten der Gesichter zu unterscheiden. Dazu wurde ein Bilderfassungssystem aufgebaut, um unter realen Bedingungen in einer Futterstation, neben den Grauwertdaten zweier Industriekameras, die Tiefendaten der Szene zu erfassen. In dem Pferdestall wurde eine Datenbank der Besuche von Pferden mit insgesamt 587k Frames erstellt. Die Methoden zur Detektion und Posennormalisierung der Pferdeköpfe in den Bilddaten sind speziell für die Pferde angepasst und entwickelt worden. Die Detektion der Pferdeköpfe arbeitet ausschließlich auf den Tiefendaten, um unabhängig von Fellfarbe und -zeichnung der Tiere zu sein, und erreicht auf den Vergleichsdaten eine Treffergenauigkeit von 97,4%. Die Identifikation von Individuen aus einer Gruppe von neun Pferden erreicht mit dem hier angewendeten Verfahren der Eigengesichter bereits mit geringer Auflösung und einem Lernanteil von 12,5% der Bilddaten eine Treffergenauigkeit von 97%. Diese Arbeit zeigt somit einen erfolgreichen Weg zur weitestgehend automatischen Identifikation von Pferden unter Verwendung der biometrischen Merkmale im Gesicht der Tiere auf.The recognition of human individuals based on facial images is a wide spreaded topic within research literature. Whereas there’s only very limited literature/research on the adaptation of methods to recognize individuals in livestock. Additionally no sound scientific literature deals with the detection of those animals’ heads in livestock (e.g. cattle, horse or sheep), whose eyes are placed laterally on their heads. An appropriate system being able to do animal identification on face images is a true alternative to current systems using radio-frequency identification (RFID) transponders for animal monitoring in precision livestock farming (PFL). The main objective of the current research work was to develop and apply methods to identify individual horses only based on facial camera data, which should run at the greatest possible extent automatically. To achieve this goal a device has been developed that allows the capturing of depth data of the scene in addition to grey scale data of two industrial cameras under real-life conditions within a feeding station for horses. A database of horses has been created during their visits to an automatic feeding station inside the housing. Altogether 587k frames have been captured. Special methods were developed and/or adapted to detect the individual heads of the horses and to normalize their pose in the image data automatically. The detection of the horses’ heads is based exclusively on the depth data, in order to be independent of color and pattern on the animal’s coat. It achieves an accuracy (ACC) of 97,4% on the comparative data. Identification has been performed with the method of eigenfaces on a group of nine horses reaching an accuracy (ACC) of 97% using low image resolution and 12,5% of the image data for training. The current research work was successful in showing a way for an almost completely automatic detection and identification of horses by utilizing the biometric features within the animals’ faces