![]() The National Institute of Standards and Technology (NIST) in America conducted tests on facial recognition systems that digitally capture facial images and then cross-reference them against a photograph such as a passport or driving licence picture, indicating an accuracy of 99.3%.īut don’t forget this level of accuracy can only be achieved in ideal conditions, and as already mentioned, a flexible, constantly moving human face, variations in lighting that is altering from bright to dull and then shadowed and then glaring will undoubtedly bring that accuracy figure down. How accurate are facial recognition systems? AI will fail in certain applications without a diverse input of data, but more on that later.Įvery time AI accurately matches two facial images, it remembers the process, so when it comes to accuracy, AI facial recognition can score close to 100%. It must absorb a varied and accurate diet of data examples or datasets as they are known. It’s also true that AI is only as good as the diet it is fed to ensure that its deep-learning abilities are fully exploited. Whereas humans, on occasion, can be defensive about admitting errors, AI absorbs the mistake and self-corrects, which is one of the principles of deep learning. So AI can and does make mistakes, but it has one significant advantage over humans when it comes to learning from them. This is particularly crucial regarding the security and criminal applications of AI. It’s fair to say these facial prints are a pretty accurate way of recognising a human face but not so smart at cross-referencing it to an identity picture, such as could be on criminal files or passports and driving licenses. Ideally, these measurements should create a facial specification as individual as your fingerprint. These include dimensions such as the distance between each eye pupil, the centre line and width of the nose, the position of the mouth relative to the eyes and nose and many others. To try and deal with these, sensors divide the face into several reference points. Add to those the changes made by the ageing process, variations in ambient lighting or lack of it and, finally, a technical and socially challenging factor, different skin pigmentations. There are operational aspects relative to facial recognition that make developing a reliable AI-operated system challenging, and these are to do with the infinite number of factors that can influence a human face.Īmongst these are emotional changes to expression that affect facial muscles, eye shapes and skin contours. Let’s forget the privacy and individual rights issues for the moment. The reason for that is the operational failures of the artificial intelligence (AI) software running many of the security and police surveillance cameras that process and identify the facial recognition images they capture and store, all of them without our prior permission. The problem is that’s not always true, thanks to facial recognition identity errors. However, a consoling thought is that if I behave myself, then so what? Nothing is going to happen to me. So, do I mind this invasion of my privacy? The blunt answer is that it doesn’t matter how I feel it’s still going to happen. ![]() Surveys have put the number of CCTV cameras in London at around 870,000, although getting a precise figure is difficult because not all cameras are officially registered. ![]() This will be by a variety of devices that can be government or police surveillance, business security, or private security systems used by property owners. If it was Germanium (likely) you can go silicone you don't need 900 Mhz switching for sure.I Live in London, which means that when I move around the city, be it for business meetings, shopping or sightseeing, I will be photographed over 200 times in one day. if you had a schematic and could highlight the transistor I am sure some gurus could give you other options. I am guessing you can put a lot of transistors in there. General Purpose Silicon Rectifier not a match NPN Transistor High Voltage Power Output not likely for a RF circuit Germanium Mesa Transistor, PNP, for High–Speed Switching Applications The NTE160 is a germanium mesa PNP transistor in a TO72 metal caseĭesigned for use as a preamplifier mixer and oscillator up to 900MHz. ![]() Germanium PNP Transistor (sounds like a likely match) The NTE is another transistor, NPN low noise high gain PNP audio power amplifier, NTE289A is NPN version (circuit should tell you PNP or NPN) I am gussing it is a VFL2744 not VFO 2744? I plugged in 2744 and got something different. Anyone know what a vfo 2744 might cross reference to? it is in the pream section of a Solid state magnavox consoleįor a 2744 I get a NTE 290A would this be correct?
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