Facial-recognition companies target schools, promising an end to shootings

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The facial-recognition cameras installed near the bounce houses at the Warehouse, an after-school recreation center in Bloomington, Indiana, are aimed low enough to scan the face of every parent, teenager and toddler who walks in. The center's director, David Weil, learned earlier this year of the surveillance system from a church newsletter, and within six weeks he had bought his own, believing it promised a security breakthrough that was both affordable and cutting-edge. Since last month, the system has logged thousands of visitors' faces – alongside their names, phone numbers and other personal details – and checked them against a regularly updated blacklist of sex offenders and unwanted guests. The system's Israeli developer, Face-Six, also promotes it for use in prisons and drones. "Some parents still think it's kind of '1984,' " said Weil, whose 21-month-old granddaughter is among the scanned.


Company Offers Free Facial Recognition Software to Boost School Security

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With the wave of school shootings that have swept the U.S. in recent years, concerns about physical security and safety have overwhelmed parents, teachers and school administrators alike. Facial recognition technology, which would allow schools and law enforcement to quickly identify who is entering their schools and when could give school districts a powerful means to make schools even safer. Last month, RealNetworks, the streaming media company that garnered attention in the '90s and early 2000s for developing the first audio streaming solution, announced it would offer its facial recognition software, SAFR, for free to over 100,000 school districts. "School safety has become one of the top national issues in the United States in 2018," said Rob Glaser, chairman and CEO of RealNetworks in a press release. "We are proud to give our leading-edge SAFR for K-12 technology solution to every elementary, middle, and high school in America and Canada.


AI-enabled image recognition system to revolutionize the manufacturing line

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Working hands-on with this technology for five years with Fujitsu Group companies, Fujitsu Laboratories has made progress improving the productivity, quality, cost and delivery of electronics parts manufacturing. Download our document to learn more.


Japan reveals plan to use facial recognition security system at 2020 Olympic Games

Daily Mail - Science & tech

A facial recognition system will be used across an Olympics for the first time as Tokyo organizers work to keep security tight and efficient during the 2020 Games. The NeoFace technology, developed by NEC Corp., will not be used on spectators. Instead, it will be customized to monitor athletes, officials, staff and media at over 40 venues, games villages and media centers. The system was officially unveiled by Olympic and company officials on Tuesday. Local organizers said Tokyo will be the first Olympic host to introduce the face recognition technology at all venues.


The IBM Speaker Recognition System: Recent Advances and Error Analysis

arXiv.org Machine Learning

We present the recent advances along with an error analysis of the IBM speaker recognition system for conversational speech. Some of the key advancements that contribute to our system include: a nearest-neighbor discriminant analysis (NDA) approach (as opposed to LDA) for intersession variability compensation in the i-vector space, the application of speaker and channel-adapted features derived from an automatic speech recognition (ASR) system for speaker recognition, and the use of a DNN acoustic model with a very large number of output units (~10k senones) to compute the frame-level soft alignments required in the i-vector estimation process. We evaluate these techniques on the NIST 2010 SRE extended core conditions (C1-C9), as well as the 10sec-10sec condition. To our knowledge, results achieved by our system represent the best performances published to date on these conditions. For example, on the extended tel-tel condition (C5) the system achieves an EER of 0.59%. To garner further understanding of the remaining errors (on C5), we examine the recordings associated with the low scoring target trials, where various issues are identified for the problematic recordings/trials. Interestingly, it is observed that correcting the pathological recordings not only improves the scores for the target trials but also for the nontarget trials.