Most people are surprised to learn that face recognition was invented way back in the 1960s. In the beginning, it wasn’t very useful. Nor was it very useful or dependable in the 1970s, 1980s, 1990s or even in the early 2000s. Early adopters included banks, event managers and forensic investigators and law enforcement agencies that tried to use it unsuccessfully, resulting in lots of failure, frustration and bad press because the technology wasn’t truly ready for prime time.
Only in the past few years has face recognition become accurate and fast enough to begin to fulfill the dreams that futurists had decades ago.
Nowadays every CCTV camera has AI Facial Recognition and some Features But lets Talk about New and Innovative Features beyond Facial Recognition using a Software Appearance Search
Appearance Search can find people based on their age, gender, clothing, and facial characteristics, and it scans through videos like facial recognition tech — though the company that makes it, Avigilon, says it doesn’t technically count as a full-fledged facial recognition tool.
Appearance Search allows school administrators to review where a person has traveled throughout campus — anywhere there’s a camera — using data the system collects about that person’s clothing, shape, size, and potentially their facial characteristics, among other factors. It also allows security officials to search through camera feeds using certain physical descriptions, like a person’s age, gender, and hair color. So while the tool can’t say who the person is, it can find where else they’ve likely been.
Here’s how Appearance Search works
Imagine you’re a school safety officer monitoring live video-camera feeds on campus. You see a young person you don’t recognize doing something suspicious in a hallway. From your computer, you click on that person’s body. Based on details about that student, like their gender, age, clothing, hair, and potentially what Avigilon calls facial characteristics, you can use Avigilon’s artificial intelligence to mine through video footage collected from cameras all over the school, looking for all instances where someone who resembles that person appears. (Avigilon also sells an AI-based system that detects unusual motion, which, for instance, could notice a student moving through a normally deserted hallway).
Avigilon says Appearance Search isn’t facial recognition. The images aren’t being tied to a particular person’s name or identity, and while the tool can use facial characteristics, it also relies on other aspects of a person’s body to do its job. In an email, a spokesperson says its Appearance Search software “only help[s] measure and rank how similar a pair of images are and [does] not associate the signature with the identity or name of a specific person.”
Avigilon’s surveillance tool exists in a gray area: Even privacy experts are conflicted over whether or not it would be accurate to call the system facial recognition. After looking at publicly available content about Avigilon, Leong said it would be fairer to call the system an advanced form of characterization, meaning that the system is making judgments about the attributes of that person, like what they’re wearing or their hair, but it’s not actually claiming to know their identity.
An advertising pamphlet from Avigilon even describes the tool working in this way: One school used Appearance Search to track when a girl entered and exited the bathroom during lunch hours. That allowed a principal to find out she was eating in the bathroom because she was being bullied and then intervene.
Appearance Search could easily be used in conjunction with standard facial recognition tech. In fact, Avigilon is also rolling out a tool it does call facial recognition — Appearance Alerts
While the ability to recognize individuals in real time has become reality, in Western countries like the United States, most people in a face recognition database of documented persons of interest are included because of a prior offense or a series of them. Retailers, for example, commonly apprehend people attempting to steal from their stores. These individuals are often photographed, and the ensuing images are uploaded into a private face recognition database. Because most shoplifters are often serial offenders – the average shoplifter steals 48 times before they are caught – it is extremely likely that the person will return, at which time an alert directs in-store security to observe the individual or offer customer service. The result is reduced theft and a much smaller chance of violence. In this way, face recognition has partially fulfilled Dick’s vision of using technology to prevent crime.
Thanks to improvements in camera technology, mapping processes, machine learning and processing speeds, facial recognition has come of age.But do we need Apperance Search which invades Our Privacy all the Time.