3,334 research outputs found
Rail Passenger Selective Screening Summit, MTI S-09-01
This publication is an edited transcript of the Rail Passenger Selective Screening Summit, which was co-sponsored by MTI and the American Public Transportation Association (APTA) in Chicago, Illinois on June 18, 2009, during APTAÂŽs annual Rail Conference. The workshop was moderated by Brian Michael Jenkins, director, Mineta Transportation Institute\u27s National Transportation Security Center of Excellence (NTSCOE). Speakers included Bruce R. Butterworth, co-author, Selective Screening of Rail Passengers; Greg Hull, president, American Public Transportation Association (APTA); Paul MacMillan, chief of police, Massachusetts Bay Transportation Authority, Transit Police Department; Ron Masciana, deputy chief, Metropolitan Transit Authority (MTA), New York; Jesus Ojeda, security coordinator, Southern California Regional Rail Authority; Ed Phillips, operations deputy, Office of Security, Amtrak; and John P. Sammon, assistant administrator, Transportation Sector Network Management, Transportation Security Administration (TSA
CN-Celeb-AV: A Multi-Genre Audio-Visual Dataset for Person Recognition
Audio-visual person recognition (AVPR) has received extensive attention.
However, most datasets used for AVPR research so far are collected in
constrained environments, and thus cannot reflect the true performance of AVPR
systems in real-world scenarios. To meet the request for research on AVPR in
unconstrained conditions, this paper presents a multi-genre AVPR dataset
collected `in the wild', named CN-Celeb-AV. This dataset contains more than
419k video segments from 1,136 persons from public media. In particular, we put
more emphasis on two real-world complexities: (1) data in multiple genres; (2)
segments with partial information. A comprehensive study was conducted to
compare CN-Celeb-AV with two popular public AVPR benchmark datasets, and the
results demonstrated that CN-Celeb-AV is more in line with real-world scenarios
and can be regarded as a new benchmark dataset for AVPR research. The dataset
also involves a development set that can be used to boost the performance of
AVPR systems in real-life situations. The dataset is free for researchers and
can be downloaded from http://cnceleb.org/.Comment: INTERSPEECH 202
Machine Learning for Biometrics
Biometrics aims at reliable and robust identification of humans from their personal traits, mainly for security and authentication purposes, but also for identifying and tracking the users of smarter applications. Frequently considered modalities are fingerprint, face, iris, palmprint and voice, but there are many other possible biometrics, including gait, ear image, retina, DNA, and even behaviours. This chapter presents a survey of machine learning methods used for biometrics applications, and identifies relevant research issues. We focus on three areas of interest: offline methods for biometric template construction and recognition, information fusion methods for integrating multiple biometrics to obtain robust results, and methods for dealing with temporal information. By introducing exemplary and influential machine learning approaches in the context of specific biometrics applications, we hope to provide the reader with the means to create novel machine learning solutions to challenging biometrics problems
Multibiometric security in wireless communication systems
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/08/2010.This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and
WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition.
First is the enrolment phase by which the database of watermarked fingerprints with
memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel.
Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present oneâs fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user.
The following three steps then involve speaker recognition including the user
responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user.
In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint
image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and
sliding neighborhood) have been followed with further two steps for embedding, and
extracting the watermark into the enhanced fingerprint image utilising Discrete
Wavelet Transform (DWT).
In the speaker recognition stage, the limitations of this technique in wireless
communication have been addressed by sending voice feature (cepstral coefficients)
instead of raw sample. This scheme is to reap the advantages of reducing the
transmission time and dependency of the data on communication channel, together
with no loss of packet. Finally, the obtained results have verified the claims
Face Recognition: Issues, Methods and Alternative Applications
Face recognition, as one of the most successful applications of image analysis, has recently gained significant attention. It is due to availability of feasible technologies, including mobile solutions. Research in automatic face recognition has been conducted since the 1960s, but the problem is still largely unsolved. Last decade has provided significant progress in this area owing to advances in face modelling and analysis techniques. Although systems have been developed for face detection and tracking, reliable face recognition still offers a great challenge to computer vision and pattern recognition researchers. There are several reasons for recent increased interest in face recognition, including rising public concern for security, the need for identity verification in the digital world, face analysis and modelling techniques in multimedia data management and computer entertainment. In this chapter, we have discussed face recognition processing, including major components such as face detection, tracking, alignment and feature extraction, and it points out the technical challenges of building a face recognition system. We focus on the importance of the most successful solutions available so far. The final part of the chapter describes chosen face recognition methods and applications and their potential use in areas not related to face recognition
An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation
Speech enhancement and speech separation are two related tasks, whose purpose
is to extract either one or more target speech signals, respectively, from a
mixture of sounds generated by several sources. Traditionally, these tasks have
been tackled using signal processing and machine learning techniques applied to
the available acoustic signals. Since the visual aspect of speech is
essentially unaffected by the acoustic environment, visual information from the
target speakers, such as lip movements and facial expressions, has also been
used for speech enhancement and speech separation systems. In order to
efficiently fuse acoustic and visual information, researchers have exploited
the flexibility of data-driven approaches, specifically deep learning,
achieving strong performance. The ceaseless proposal of a large number of
techniques to extract features and fuse multimodal information has highlighted
the need for an overview that comprehensively describes and discusses
audio-visual speech enhancement and separation based on deep learning. In this
paper, we provide a systematic survey of this research topic, focusing on the
main elements that characterise the systems in the literature: acoustic
features; visual features; deep learning methods; fusion techniques; training
targets and objective functions. In addition, we review deep-learning-based
methods for speech reconstruction from silent videos and audio-visual sound
source separation for non-speech signals, since these methods can be more or
less directly applied to audio-visual speech enhancement and separation.
Finally, we survey commonly employed audio-visual speech datasets, given their
central role in the development of data-driven approaches, and evaluation
methods, because they are generally used to compare different systems and
determine their performance
- âŠ