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		<title><![CDATA[ Biometrics, IET - new TOC ]]></title>
		<link>http://ieeexplore.ieee.org</link>
		<description>TOC Alert for Publication# 6072579 </description>
		<year>2013</year>
		<month>May      </month>
		<day>23</day>
		<item>
			<title><![CDATA[Improving tactical biometric systems through the application of systems engineering]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6518005]]></link>
			<description><![CDATA[To date, the impact of tactical biometric systems has been limited by designs driven by subsystem performance metrics and little consideration for the operational environment in which they are deployed. The design of these systems may be significantly improved by the application of systems engineering practices that consider these and other factors. This study discusses limitations of the current system design approach and proposes a methodology to improve designs. These improvements in design have the potential to dramatically increase the effectiveness and acceptance of biometric technologies in operational environments.]]></description>
			<pubDate><![CDATA[March  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6518005]]></guid>
			<volume>2</volume>
			<issue>1</issue>
			<startPage>1</startPage>
			<endPage>9</endPage>
			<fileSize>456</fileSize>
			<authors><![CDATA[Faddis, K.N.;Matey, J.R.;Stracener, J.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Improved average of synthetic exact filters for precise eye localisation under realistic conditions]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6518006]]></link>
			<description><![CDATA[Precise eye localisation is a crucial step for many applications, including face recognition, gaze tracking and blink detection. In this study, the authors propose several improvements to the original average of synthetic exact filters (ASEF) formulation, demonstrating that its accuracy can be enhanced if adequate illumination correction, spatial priors and crossfilter responses are exploited for eye localisation. The so-called improved ASEF (iASEF) was tested on the well-known BioID database and other more challenging datasets comprising real world face imagery: labelled faces in the wild (LFW) and the very recent labelled face parts in the wild. The iASEF provides the state-of-the-art results, ranking first on BioID database and second on a 2000-image LFW subset. In addition, the authors propose a novel, much more challenging benchmark for eye localisation using the whole LFW and a standard protocol initially designed for face verification. Improvements over original ASEF were also confirmed on this difficult test, although with a significant drop in performance. They point out the necessity of adopting these realistic validation scenarios, in order to evaluate the actual state-of-the-art and fairly compare eye localisation methods in unconstrained settings, where localisation accuracy is still far from perfect.]]></description>
			<pubDate><![CDATA[March  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6518006]]></guid>
			<volume>2</volume>
			<issue>1</issue>
			<startPage>10</startPage>
			<endPage>20</endPage>
			<fileSize>693</fileSize>
			<authors><![CDATA[Vazquez-Fernandez, E.;Gonzalez-Jimenez, D.;Yu, L.L.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Singular value decomposition and wavelet-based iris biometric watermarking]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6518007]]></link>
			<description><![CDATA[These days, with technological advancement, it is very easy for miscreants to produce illegal multimedia data copies. Various techniques of copyright protection of free data are being developed daily. Digital watermarking is one such technique, where digital embedding of the copyright information/watermark into the data to be protected. The two major ways of doing so are spatial domain and the robust transform domain. In this study, method for watermarking of digital images, with biometric data is presented. The usage of biometric instead of the traditional watermark increases the security of the image data. The biometric used here is iris. After the retinal scan, it is the most unique biometric. In terms of user friendliness in extracting the biometric, it comes after fingerprint and facial scan. The iris biometric template is generated from subject??s eye images. The discrete cosine values of templates are extracted through discrete cosine transform and converted to binary code. This binary code is embedded in the singular values of the host image's coefficients generated through wavelet transform. The original image is thus firstly applied with the discrete wavelet transform followed up by the singular value decomposition of the subband coefficients. The algorithm has been tested with popular attacks for analysis of false recognition and rejection of subjects.]]></description>
			<pubDate><![CDATA[March  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6518007]]></guid>
			<volume>2</volume>
			<issue>1</issue>
			<startPage>21</startPage>
			<endPage>27</endPage>
			<fileSize>744</fileSize>
			<authors><![CDATA[Majumder, S.;Devi, K.J.;Sarkar, S.K.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Gender classification via lips: static and dynamic features]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6518008]]></link>
			<description><![CDATA[Automatic gender classification has many security and commercial applications. Various modalities have been investigated for gender classification with face-based classification being the most popular. In some real-world scenarios the face may be partially occluded. In these circumstances a classification based on individual parts of the face known as local features must be adopted. The authors investigate gender classification using lip movements. They show for the first time that important gender-specific information can be obtained from the way in which a person moves their lips during speech. Furthermore, this study indicates that the lip dynamics during speech provide greater gender discriminative information than simply lip appearance. They also show that the lip dynamics and appearance contain complementary gender information such that a model which captures both traits gives the highest overall classification result. They use discrete cosine transform-based features and Gaussian mixture modelling to model lip appearance and dynamics and employ the XM2VTS database for their experiments. These experiments show that a model which captures lip dynamics along with appearance can improve gender classification rates by between 16 and 21% compared with models of only lip appearance.]]></description>
			<pubDate><![CDATA[March  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6518008]]></guid>
			<volume>2</volume>
			<issue>1</issue>
			<startPage>28</startPage>
			<endPage>34</endPage>
			<fileSize>537</fileSize>
			<authors><![CDATA[Stewart, D.;Pass, A.;Zhang, J.;]]></authors>
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