147 research outputs found
Face Liveness Detection under Processed Image Attacks
Face recognition is a mature and reliable technology for identifying people. Due
to high-definition cameras and supporting devices, it is considered the fastest and
the least intrusive biometric recognition modality. Nevertheless, effective spoofing
attempts on face recognition systems were found to be possible. As a result, various anti-spoofing algorithms were developed to counteract these attacks. They are
commonly referred in the literature a liveness detection tests. In this research we highlight the effectiveness of some simple, direct spoofing attacks, and test one of
the current robust liveness detection algorithms, i.e. the logistic regression based face liveness detection from a single image, proposed by the Tan et al. in 2010, against malicious attacks using processed imposter images. In particular, we study experimentally the effect of common image processing operations such as sharpening and smoothing, as well as corruption with salt and pepper noise, on the face liveness detection algorithm, and we find that it is especially vulnerable against spoofing attempts using processed imposter images. We design and present a new facial database, the Durham Face Database, which is the first, to the best of our knowledge, to have client, imposter as well as processed imposter images. Finally, we evaluate our claim on the effectiveness of proposed imposter image attacks using transfer learning on Convolutional Neural Networks. We verify that such attacks are more difficult to detect even when using high-end, expensive machine learning techniques
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)
De-Duplication of Person's Identity Using Multi-Modal Biometrics
The objective of this work is to explore approaches to create unique identities by the de-duplication process using multi-modal biometrics. Various government sectors
in the world provide different services and welfare schemes for the beneffit of the people in the society using an identity number. A unique identity (UID) number assigned for
every person would obviate the need for a person to produce multiple documentary proofs of his/her identity for availing any government/private services. In the process
of creating unique identity of a person, there is a possibility of duplicate identities as the same person might want to get multiple identities in order to get extra beneffits from the Government. These duplicate identities can be eliminated by the de-duplication process using multi-modal biometrics, namely, iris, ngerprint, face and signature. De-duplication is the process of removing instances of multiple enrollments of the same person using the person's biometric data. As the number of people enrolledinto the biometric system runs into billions, the time complexity increases in the de duplication process. In this thesis, three different case studies are presented to address the performance issues of de-duplication process in order to create unique identity of a person
Biometrics
Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book
Unifying the Visible and Passive Infrared Bands: Homogeneous and Heterogeneous Multi-Spectral Face Recognition
Face biometrics leverages tools and technology in order to automate the identification of individuals. In most cases, biometric face recognition (FR) can be used for forensic purposes, but there remains the issue related to the integration of technology into the legal system of the court. The biggest challenge with the acceptance of the face as a modality used in court is the reliability of such systems under varying pose, illumination and expression, which has been an active and widely explored area of research over the last few decades (e.g. same-spectrum or homogeneous matching). The heterogeneous FR problem, which deals with matching face images from different sensors, should be examined for the benefit of military and law enforcement applications as well. In this work we are concerned primarily with visible band images (380-750 nm) and the infrared (IR) spectrum, which has become an area of growing interest.;For homogeneous FR systems, we formulate and develop an efficient, semi-automated, direct matching-based FR framework, that is designed to operate efficiently when face data is captured using either visible or passive IR sensors. Thus, it can be applied in both daytime and nighttime environments. First, input face images are geometrically normalized using our pre-processing pipeline prior to feature-extraction. Then, face-based features including wrinkles, veins, as well as edges of facial characteristics, are detected and extracted for each operational band (visible, MWIR, and LWIR). Finally, global and local face-based matching is applied, before fusion is performed at the score level. Although this proposed matcher performs well when same-spectrum FR is performed, regardless of spectrum, a challenge exists when cross-spectral FR matching is performed. The second framework is for the heterogeneous FR problem, and deals with the issue of bridging the gap across the visible and passive infrared (MWIR and LWIR) spectrums. Specifically, we investigate the benefits and limitations of using synthesized visible face images from thermal and vice versa, in cross-spectral face recognition systems when utilizing canonical correlation analysis (CCA) and locally linear embedding (LLE), a manifold learning technique for dimensionality reduction. Finally, by conducting an extensive experimental study we establish that the combination of the proposed synthesis and demographic filtering scheme increases system performance in terms of rank-1 identification rate
Comparing Features of Three-Dimensional Object Models Using Registration Based on Surface Curvature Signatures
This dissertation presents a technique for comparing local shape properties for similar three-dimensional objects represented by meshes. Our novel shape representation, the curvature map, describes shape as a function of surface curvature in the region around a point. A multi-pass approach is applied to the curvature map to detect features at different scales. The feature detection step does not require user input or parameter tuning. We use features ordered by strength, the similarity of pairs of features, and pruning based on geometric consistency to efficiently determine key corresponding locations on the objects. For genus zero objects, the corresponding locations are used to generate a consistent spherical parameterization that defines the point-to-point correspondence used for the final shape comparison
Biometric Systems
Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications
Face age estimation using wrinkle patterns
Face age estimation is a challenging problem due to the variation of craniofacial growth,
skin texture, gender and race. With recent growth in face age estimation research, wrinkles
received attention from a number of research, as it is generally perceived as aging
feature and soft biometric for person identification. In a face image, wrinkle is a discontinuous
and arbitrary line pattern that varies in different face regions and subjects.
Existing wrinkle detection algorithms and wrinkle-based features are not robust for face
age estimation. They are either weakly represented or not validated against the ground
truth. The primary aim of this thesis is to develop a robust wrinkle detection method
and construct novel wrinkle-based methods for face age estimation. First, Hybrid Hessian
Filter (HHF) is proposed to segment the wrinkles using the directional gradient
and a ridge-valley Gaussian kernel. Second, Hessian Line Tracking (HLT) is proposed
for wrinkle detection by exploring the wrinkle connectivity of surrounding pixels using a
cross-sectional profile. Experimental results showed that HLT outperforms other wrinkle
detection algorithms with an accuracy of 84% and 79% on the datasets of FORERUS
and FORERET while HHF achieves 77% and 49%, respectively. Third, Multi-scale
Wrinkle Patterns (MWP) is proposed as a novel feature representation for face age
estimation using the wrinkle location, intensity and density. Fourth, Hybrid Aging Patterns
(HAP) is proposed as a hybrid pattern for face age estimation using Facial Appearance
Model (FAM) and MWP. Fifth, Multi-layer Age Regression (MAR) is proposed as
a hierarchical model in complementary of FAM and MWP for face age estimation. For
performance assessment of age estimation, four datasets namely FGNET, MORPH,
FERET and PAL with different age ranges and sample sizes are used as benchmarks.
Results showed that MAR achieves the lowest Mean Absolute Error (MAE) of 3.00
( 4.14) on FERET and HAP scores a comparable MAE of 3.02 ( 2.92) as state of the
art. In conclusion, wrinkles are important features and the uniqueness of this pattern
should be considered in developing a robust model for face age estimation
Driver attention and behaviour monitoring with the Microsoft Kinect sensor
Modern vehicles are designed to protect occupants in the event of a crash with some vehicles better at this than others. However, passenger protection during an accident has shown to be not enough in many high impact crashes. Statistics have shown that the human error is the number one contributor to road accidents. This research study explores how driver error can be reduced through technology which observes driver behaviour and reacts when certain unwanted patterns in behaviour have been detected. Finally a system that detects driver fatigue and driver distraction has been developed using non-invasive machine vision concepts to monitor observable driver behaviour.Electrical EngineeringM. Tech. (Electrical Engineering
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