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Chapter and Conference Paper
On-Device Learning with Binary Neural Networks
Existing Continual Learning (CL) solutions only partially address the constraints on power, memory and computation of the deep learning models when deployed on low-power embedded CPUs. In this paper, we propos...
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Chapter and Conference Paper
Region Prediction for Efficient Robot Localization on Large Maps
Recognizing already explored places (a.k.a. place recognition) is a fundamental task in Simultaneous Localization and Map** (SLAM) to enable robot relocalization and loop closure detection. In topological SL...
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Chapter and Conference Paper
Combining Identity Features and Artifact Analysis for Differential Morphing Attack Detection
Due to the importance of the Morphing Attack, the development of new and accurate Morphing Attack Detection (MAD) systems is urgently needed by private and public institutions. In this context, D-MAD methods, i.e...
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Chapter and Conference Paper
Input Layer Binarization with Bit-Plane Encoding
Binary Neural Networks (BNNs) use 1-bit weights and activations to efficiently execute deep convolutional neural networks on edge devices. Nevertheless, the binarization of the first layer is conventionally ex...
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Chapter
Fingerprint Sensing
This chapter surveys available fingerprint acquisition techniques: from the traditional “ink on paper” to live-scan sensing based on optical, capacitive, and ultrasonic technologies. Technological advancements...
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Chapter
Fingerprint Matching
This chapter formalizes the fingerprint matching problem, namely finding a similarity between any given fingerprint pair. The requirements of the matching problem are high similarity values between two impress...
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Chapter
Latent Fingerprint Recognition
Latent fingerprints (or fingermarks) are the traces of friction ridges left by fingers touching object surfaces. Because of their poor quality, latent fingerprints have been (and still often are) processed sem...
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Chapter
Fingerprint Individuality
Scientific evidence supporting fingerprint individuality (i.e., quantifying the extent of uniqueness of a fingerprint) is being increasingly demanded, particularly in forensic applications when a suspect is tr...
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Chapter
Introduction
This chapter presents an introduction to biometric and, in particular, fingerprint recognition systems and provides some historical timeline on fingerprints and their adoption in forensic and civilian recognit...
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Chapter
Fingerprint Analysis and Representation
This chapter introduces classical domain knowledge-based and emerging learning-based techniques for feature extraction in fingerprints. Specific sections are dedicated to explain the most effective approaches ...
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Book
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Chapter
Fingerprint Classification and Indexing
This chapter focuses on fingerprint type classification and indexing techniques designed to speed up fingerprint identification on large databases. Exclusive classification techniques are first reviewed and th...
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Chapter
Fingerprint Synthesis
Synthetic fingerprints, when properly generated, represent a reasonable substitute for real fingerprints for the design, training, and benchmarking of fingerprint recognition algorithms. This approach is parti...
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Chapter
Securing Fingerprint Systems
This chapter discusses the security issues and countermeasures that can be used to build secure fingerprint recognition systems. After an introduction to the different types of attacks that can be potentially ...
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Chapter and Conference Paper
Gesture Recognition by Leap Motion Controller and LSTM Networks for CAD-oriented Interfaces
This paper presents a gesture recognition approach for CAD interfaces where the Leap Motion Controller is used for its high precision in modelling user hands. A simple, compact and effective hand representatio...
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Chapter
Automated Fingerprint Identification Systems: From Fingerprints to Fingermarks
The aim of this chapter is to present the automated recognition technology and its use for forensic applications. After a brief historical review, we provide an introduction to modern ) by discussing thei...
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Chapter and Conference Paper
Comparing Incremental Learning Strategies for Convolutional Neural Networks
In the last decade, Convolutional Neural Networks (CNNs) have shown to perform incredibly well in many computer vision tasks such as object recognition and object detection, being able to extract meaningful hi...
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Chapter
On the Effects of Image Alterations on Face Recognition Accuracy
Face recognition in controlled environments is nowadays considered rather reliable, and if face is acquired in proper conditions, a good accuracy level can be achieved by state-of-the-art systems. However, we ...
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Reference Work Entry In depth
Fingerprint Recognition, Overview
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Chapter and Conference Paper
Saliency-Based Keypoint Reduction for Augmented-Reality Applications in Smart Cities
In this paper we show that Saliency-based keypoint selection makes natural landmark detection and object recognition quite effective and efficient, thus enabling augmented reality techniques in a plethora of a...
