This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding, offering valuable insights from an international selection of experts in the field. The text presents a diverse selection of novel techniques, covering applications of object recognition, face recognition, and action and event recognition. Topics and features: reviews the domain adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach; introduces a novel unsupervised method for image-to-image translation, and a video segment retrieval model that utilizes ensemble learning; proposes a unique way to determine which dataset is most useful in the base training, in order to improve the transferability of deep neural networks; describes a quantitative method for estimating the discrepancy between the source and target data to enhance image classification performance; presents a technique for multi-modal fusion that enhances facial action recognition, and a framework for intuition learning in domain adaptation; examines an original interpolation-based approach to address the issue of tracking model degradation in correlation filter-based methods. This authoritative work will serve as an invaluable reference for researchers and practitioners interested in machine learning-based visual recognition and understanding.

The challenge of a fully automatic fingerprint-recognition system continues to motivate many researchers to address fingerprints and related problems. In addition to fingerprint analysis in law enforcement, civilian applications are now being planned because of unique benefits that fingerprint technology offers. Automatic Fingerprint Recognition Systems assesses the challenges for achieving fully automatic operation. In addition, it examines advances in the various aspects of fingerprint recognition: newer models of individuality analysis, new technologies for inkless sensors, empirical evaluation of fingerprint recognition performance, synthetic generation of fingerprint images, fingerprint videos, and new large-scale systems for fingerprint identification. The book offers a history of fingerprint research and also confronts its future--in particular, how research in the allied fields of computer vision, image processing, pattern recognition, and empirical performance evaluation will advance the science and technology of fingerprint identification.

This book thoroughly surveys and examines advances in fingerprint sensing devices and in algorithms for fingerprint image analysis and matching. After an opening chapter on the history of fingerprint recognition, "Automatic Fingerprint Recognition Systems" moves into new technologies for inkless sensors, fingerprint image analysis techniques, including fingerprint video analysis, filtering and classification and other areas aimed at fully automatic operation. The book also addresses large-scale fingerprint identification system design, as well as standards.

Topics and Features:

* Covers numerous areas related to modern automatic fingerprint recognition, not just its history or forensic analysis

* Examines advances in fingerprint sensing and fingerprint image filtering and preprocessing

* Describes fingerprint feature abstraction, as well as compression and decompression of fingerprint images

* Develops ideas related to large-scale, large-database fingerprint matching

* Assesses such important areas as security in fingerprint matching and the common criterion protection profile

This authoritative survey provides a unique reference for automatic fingerprint recognition concepts, technologies, and systems. Its editors and contributors are leading researchers and applied R&D developers of this technology. Biometrics and pattern recognition researchers, security professionals, and systems developers will find the work an indispensable resource for current knowledge and technology.

This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding, offering valuable insights from an international selection of experts in the field. The text presents a diverse selection of novel techniques, covering applications of object recognition, face recognition, and action and event recognition. Topics and features: reviews the domain adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach; introduces a novel unsupervised method for image-to-image translation, and a video segment retrieval model that utilizes ensemble learning; proposes a unique way to determine which dataset is most useful in the base training, in order to improve the transferability of deep neural networks; describes a quantitative method for estimating the discrepancy between the source and target data to enhance image classification performance; presents a technique for multi-modal fusion that enhances facial action recognition, and a framework for intuition learning in domain adaptation; examines an original interpolation-based approach to address the issue of tracking model degradation in correlation filter-based methods. This authoritative work will serve as an invaluable reference for researchers and practitioners interested in machine learning-based visual recognition and understanding.