A sell-out crowd of 45,000 spectators watching the fifth and final Ashes Test in Sydney this week is, in turn, being watched by a team of security professionals at the Sydney Cricket Ground (SCG). For the first time, the SCG's security team is utilising 820 new cameras equipped with facial recognition technology to scrutinise the crowd for safety threats. The cameras, which feed into an upgraded operations centre inside the ground, allow security personnel to monitor patrons as they approach the ground and while they're inside the venue, an SCG Trust spokesman said on Thursday. The AU$3.5 million upgrade to security includes a new video analytics system that can detect and zoom in on unattended bags, suspicious vehicles, and strange behaviour. A trial in 2017 allowed police and security to intercept six banned spectators as they tried to enter the SCG.
CNL Software has entered into a technology partnership with Herta Security under the CNL Software Technology Alliance Program. Herta develops user-friendly software solutions that enable the integration of facial recognition in security applications. According to the announcement, Herta's deep learning algorithms encode faces directly into small templates, which are very fast to compare and yield more accurate results. This provides a technological advantage when working with partners, as it allows the development of more robust, safer and efficient solutions. IPSecurityCenter PSIM takes a vendor agnostic approach to implement flexible and scalable security management software.
Dubai: Thousands of CCTV cameras of various Dubai government agencies will now provide live feed to a central command centre, officials said. Under a new Artificial Intelligence (AI) network, security cameras across will relay live images of security breaches live to the central command centre, Dubai Police said. The cameras will monitor criminal behaviour in three sectors -- tourism, traffic and bricks and mortar facilities. The network, said the police, is being phased in via different stages to meet the Dubai 2021 Vision requirements of a smart city. Announcing the programme, Major-General Khalil Ebrahim Al Mansouri, Assistant Commander-in-Chief for Criminal Investigation Affairs, said the new project called'Oyoon' (eyes) will tackle crimes in the city and help reduce traffic accident deaths and congestion.
ABSTRACT Considering the existence of very large amount of available data repositories and reach to the very advanced system of hardware, systems meant for facial identification have evolved enormously over the past few decades. Sketch recognitio n is one of the most important areas that have evolved as an integral component adopted by the agencies of law administration in curren t trends of forensic science. Matching of derived sketches to photo images of face is also a difficult assignment as the considered sketches are produced upon the verbal explanation depicted by the eye witness of the crime scene and may have scarcity of se nsitive elements that exist in the photograph as one can accurately depict due to the natural human error. Substantial amount of the novel research work carried out in this area up late used recognition system through traditional extraction and classificat ion models . But very recently, few researches work focused on using deep learning techniques to take an advantage of learning models for the feature extraction and classification to rule out potential domain challenges. The first part of this review paper basically focuses on deep learning techniques used in face recognition and matching which as improved the accuracy of face recognition technique with training of huge sets of data. This paper also includes a survey on different techniques used to match com posite sketches to human images which includes component - based representation approach, automatic composite sketch recognition technique etc. INTRODUCTION As per the researches carried out, a complete face recognition system includes two patterns of face detection and face recognition: 1) Structural similarity and 2) individual local differences of human faces. Therefore, it is required to extract the features of the face through the face detection process. The evolution of face recognition is due to its technical challenges and huge potential application in video surveillance, identity authorization, multimedia applications, home and office security, law enforcement and different human - computer interaction activities. Facial recognition technology (FRT) is one of the most controversial new tools. It was first devel oped in the 1960s.
National Crime Records Bureau (NCRB) under the Government of India has released a tender asking for bidders to help create Automated Facial Recognition System (AFRS). The objective is to leverage the power of facial recognition technology to make the security forces more efficient. The technology system which has been proposed will make an extensive database of photos belonging to Indian citizens using which machine learning models will be trained. The criminals will be identified using CCTV footage from the database, verified, and information will be distributed to all law enforcement agencies in real-time across the country. Experts have touted that using facial recognition technology to identify and solve crime may be one of the best applications of the technology.