Preface

With a very security-conscious society, biometrics-based authentication and identification have become the focus of many important applications as it is widely believed that biometrics can provide accurate and reliable identification. Biometrics research and technology continue to mature rapidly, driven by pressing industrial and government needs and supported by industrial and government funding. As the number and types of biometrics architectures, sensors, and techniques increases, the need to disseminate research results increases as well. Advanced deep learning capabilities, and deep convolutional neural networks (CNN) in particular, are significantly advancing the state of the art in computer vision and pattern recognition. The deep CNN is a biologically inspired variant of multilayer perceptron and represents a typical deep learning architecture. Since 2006, we have organized a series of high-quality Annual Biometrics Workshops under the auspices of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). This series has emerged as the premier forum for showcasing cutting-edge research from academia, industry, and government laboratories. During the past few years the CNN-based techniques, as evidenced by the increasing number of papers at CVPR and the biometrics workshop, have shown strong ability to learn effective feature representation from input data, especially for the perceptual and biometrics-related tasks. The book is based on a selection of topics and authors from the proceedings of the 2016 biometrics workshop and a general call for chapters to the computer vision, pattern recognition, and the biometrics communities at large. The selection of chapters in this book is made after two rounds of rigorous review process.