32,373 research outputs found
Forensic Face Recognition: A Survey
Beside a few papers which focus on the forensic aspects of automatic face recognition, there is not much published about it in contrast to the literature on developing new techniques and methodologies for biometric face recognition. In this report, we review forensic facial identification which is the forensic experts‟ way of manual facial comparison. Then we review famous works in the domain of forensic face recognition. Some of these papers describe general trends in forensics [1], guidelines for manual forensic facial comparison and training of face examiners who will be required to verify the outcome of automatic forensic face recognition system [2]. Some proposes theoretical framework for application of face recognition technology in forensics [3] and automatic forensic facial comparison [4, 5]. Bayesian framework is discussed in detail and it is elaborated how it can be adapted to forensic face recognition. Several issues related with court admissibility and reliability of system are also discussed. \ud
Until now, there is no operational system available which automatically compare image of a suspect with mugshot database and provide result usable in court. The fact that biometric face recognition can in most cases be used for forensic purpose is true but the issues related to integration of technology with legal system of court still remain to be solved. There is a great need for research which is multi-disciplinary in nature and which will integrate the face recognition technology with existing legal systems. In this report we present a review of the existing literature in this domain and discuss various aspects and requirements for forensic face recognition systems particularly focusing on Bayesian framework
Knowledge into Action: Stakeholders gather to discuss how information gathered can be used to inform real change on the ground in the fight against worst forms of child labor and forced adult labor
This document is part of a digital collection provided by the Martin P. Catherwood Library, ILR School, Cornell University, pertaining to the effects of globalization on the workplace worldwide. Special emphasis is placed on labor rights, working conditions, labor market changes, and union organizing.V_Knowledge_Into_Action_Meeting_Report_June22_2009.pdf: 272 downloads, before Oct. 1, 2020
Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification
There are a number of studies about extraction of bottleneck (BN) features
from deep neural networks (DNNs)trained to discriminate speakers, pass-phrases
and triphone states for improving the performance of text-dependent speaker
verification (TD-SV). However, a moderate success has been achieved. A recent
study [1] presented a time contrastive learning (TCL) concept to explore the
non-stationarity of brain signals for classification of brain states. Speech
signals have similar non-stationarity property, and TCL further has the
advantage of having no need for labeled data. We therefore present a TCL based
BN feature extraction method. The method uniformly partitions each speech
utterance in a training dataset into a predefined number of multi-frame
segments. Each segment in an utterance corresponds to one class, and class
labels are shared across utterances. DNNs are then trained to discriminate all
speech frames among the classes to exploit the temporal structure of speech. In
addition, we propose a segment-based unsupervised clustering algorithm to
re-assign class labels to the segments. TD-SV experiments were conducted on the
RedDots challenge database. The TCL-DNNs were trained using speech data of
fixed pass-phrases that were excluded from the TD-SV evaluation set, so the
learned features can be considered phrase-independent. We compare the
performance of the proposed TCL bottleneck (BN) feature with those of
short-time cepstral features and BN features extracted from DNNs discriminating
speakers, pass-phrases, speaker+pass-phrase, as well as monophones whose labels
and boundaries are generated by three different automatic speech recognition
(ASR) systems. Experimental results show that the proposed TCL-BN outperforms
cepstral features and speaker+pass-phrase discriminant BN features, and its
performance is on par with those of ASR derived BN features. Moreover,....Comment: Copyright (c) 2019 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any current or
future media, including reprinting/republishing this material for advertising
or promotional purposes, creating new collective works, for resale or
redistribution to servers or lists, or reuse of any copyrighted component of
this work in other work
What Statutes Mean: Interpretive Lessons from Positive Theories of Communication and Legislation
How should judges interpret statutes? For some scholars and judges, interpreting statutes requires little more than a close examination of statutory language, with perhaps a dictionary and a few interpretive canons nearby. For others, statutory interpretation must be based upon an assessment of a statute\u27s underlying purpose, an evaluation of society\u27s current norms and values, or a normative objective, such as the law\u27s integrity. With such differences squarely framed in the literature, it is reasonable to ask whether anything of value can be added. We contend that there is
- …