SmartVis ********
******** ** ******* ********' ***** analytics ******* ****. ******** Face [**** ** ****** available] ** *** ******-*********** component, ***** ** ***** on ********** ******* **************** **** ************** ** ****. ***** **** acquisition, *** ******* **** **** have **-********** *** ******** of *** ********, ********* introducing * **** ******** basis ** *** ****** to ****** ****** ***** in ***-******* **********.
******** **** ** * server-based ******, ********* ******* from ********* ******* ** real ****, *** ********** alerts ** **** ***** on ************* ******** *** matching (** *** ********) a **** ******* * database ** ******** *****.
Deep ******** ***********
******* ******** ****** **** have **** * **** learning ******** ** ***** a ****** ******* ** recognize ***** ** ******** distinctive ******** **** ************* various *****. **** ** in ******** ** *********** systems ***** ********* **** to ******* ********** *** distances ******* ******* ********** of * **** (*.*.: spacing ******* ****, ******** between **** *** ****, etc.).
*** **** ******** ******** allows **** ** **** a *****, ** * lack ** * ***** faster ** ******** ** previous ********. **** **** enables ****** ******** ***** when *** **** ** the **** ** ************* different **** *** *****(*) used ** ****** ** in *** ********.
* ***** **** ** sampled ****** ******* ****** of *****, ******* ** from **** * ****** "best ****", ***** ******* Barriers ****** **** *********** to ********* ******** ** the ******, *** *** speed ** ********, ** the ****** ***** **** multiple ***** ** *** face, ********** ********* **** data *** *** ******** algorithm ** *******.
******* ******** ****** ***** capabilities ***** *** ******* well ****** *** "***-*********" checking, ***** * ****** may *** ** ***** a ****** *********** ****** is ** ***, *** they *** *** ***** to "******" **** *** system ** ******* ******** at * ****** ** passing ******* * **** obvious ***** *****.
Field ** **** **************
*** ********* ***** * minimum ** ** ****** between *** **** (~*** PPF / ~*** ***) for ******** ***********, *** **** see ******** *********** ************ up ** ** ****** ******* the ****, **** ****** numbers (*.*.: ** ******) not ******** *** ********** benefit ** ********.
** ** * ***** in *** ***** ** view *** ** ************** analyzed.
***** ***** ** ** theoretical ******* ********** *********, the * **** ***** and ***** ************ ********* lead ** ********* ***** 1080p cameras.
Enrolling *****
***** *** ** ******** in *** ******** **** static ****** ******** ("********"), from * ****** ******** to * **, ** from ** **** ***** segments. *******, ******** *****/****** of *** **** *** captured *** ******* ******** in ******** *** ******/**** later.
Server ************
******** **** **** ** an *****/******* ***** ******** (i5 ** ** *********), and **** **** ** accelerate *** **** ****** network *********. ******* **** are****** *** **** ******** cards.
***** ****** ************ **** vary ***** ** *** number ** ********, **** of *** ******** ***** database, *** *****/****** ***** desired ** *** ********. An ******* ****** **** a ****** *** **** card is ********* *** * streams, **** * ******** of ~*,*** ***** *** return ***** ******* ** real-time (GPUs *** ** ***** to ****** **** ******* simultaneously, *** *** *** CPU ************ *** ******** for ***** ********* ** applications **** **** **** throughput ** ******.
*******
***** ****** **** ~$*,***-$*,*** per *******/******, ********* ********* on *** **** ** the ****** ******** *** customer ***** ** ********. The $*,*** ***** ** most ******, ********* ** Digital ********, *** ********* running ********** ** *** range ** ******* ******* to ~*,*** *****.
Sales *******
**** **** ***** ******* *******, ******* ******** ***** the ******** ********* ******* standard ******** ********** ******** globally.
Software - **** ******** *** ** ********
***** ******** **** **** include * "**********" **** interface, ********* ** ******* Barriers, **** ********* *** the ******* **** ** an ****** **** ****** a *** ***** **** interface, ***** ***** ** a ***, ** **** other ***********-******** ********. ******* Barriers **** **** ************ with ***** **** ********* and *******, ** **** as **** **** *********, and *** **** ****** customers **** ****** ***********, if ******, *** ********** feeds. *** ******* ****** that *** ** *** variety ** ***** ******** base ***** ****** ** no "********" *******, *** that ***** ****** ****** some ****** ** ****** integration ** **** ***********.
*** ******* **** **** are ******* ** * new ******** ******* **** will **** **** ************ in *** *****-***** ******* to **** ** ********* to * ******* ******** base.
Upcoming ********
******* ******** **** **** the ******* ** ********* non-face ******* ** ** development *** ****** **** logo-tracking. **** ** **** intended ** ***** ** an ********** **** * face, *** **** ** to ***** ****** ** track **** *********** ** things **** *********** ** TV *****/******** ******, ** clothing **** ** ******, etc.
Face *********** **********
**** *********** ******* **** faced **** ********** **** the *****. **** *** technical: ************ *** *********** much ****** **** ******* surveillance ****** ******* *****, requirements *** ******* ********* of *******, *********** ** deal **** **** ***** at ****. ***** ****** are ******* ******** **** citizens, ** ****** ****** like **** ** ******* obscuring ***** *** ********** the ******* **** ******* a ***** **** ** analyze. *******, **** *** made *** ******* ****** to ******, *** ********** for ******** **** * person ** * ********* will ******** ** ******** if **** ** ****** in *****.
**** *** *** ****** any **** *********** *******, primarily *** *** ******* outlined *****, *** ****** comment ** *** ******** Face *** ******* ** real-world *********** ** ***** systems, ** ** *** own ************ *****.
Other **** *********** *********
******* ******** ** *** alone ** *** **** recognition ******** *****. ********* such **********,*****,******* **** ********* ******** face *********** ************ ** core ** ***** *********. Additionally, ***** [**** ** longer *********] *** ********* also **** **** ********* face *********** ************ ********.
** *** **** *** particular ******* **** * given **** *********** *******, please ***** * ******* detailing **** **********.
Comments (15)
Christian Laforte
I tested four or five solutions the past 10 years. They were disappointing. At best I got some of them to work reliably when doing a 1-to-1 matching, i.e. between an access card and the photo previously assigned to it. They never worked "in the wild". Mismatches were common.
This past month we have installed NEC Neoface in our office. I'm really impressed. It took a few weeks (part-time) to learn the system and tweak the many settings, mainly to accommodate some of our older, lower-resolution cameras, and running 10 cameras on an old spare server. It's now working reliably as long as subjects generally look toward a camera. NEC seems to err on the side of caution, i.e. it would rather not generate a match unless it's absolutely sure this is the right person. They have an impressive track record - some customers have databases with millions of people.
BTW organizations seriously considering face recognition should read the US NIST face recognition vendor test.
https://www.nist.gov/programs-projects/face-recognition-vendor-test-frvt
Create New Topic
Christian Laforte
> Well, what do they recommend there? It's hard to make people look towards the camera in a real world, uncontrolled surveillance application?
Two approaches that would be cheap and effective:
- Put a monitor with animated content right next to the camera, that the person is likely to look at. For instance, an animated menu (for a restaurant), a welcome message (for corporate reception), a live playback of the surveillance camera (in a retail environment to deter theft), a quote-of-the-day for a church, live sports (in a bar) etc.
- Put the camera next to an intercom, a door bell, or other device that a person will naturally face when interacting with it.
> Also, any sense of how well or poorly it scales up?
I haven't tested it yet but the system appears well designed to scale to millions of subjects. Their technical documentation is extensive and gives lots of details on how to achieve it. From a performance standpoint their software services architecture is quite similar to ours (that scaled to 100 servers for one big project). As for the accuracy, the NIST report is best suited to answer your question.
> Btw that I mean, it's easier to get good results with a small watchlist and a small number of people being surveilled but put it in a large scale application with tens of thousands of different people passing daily and hundreds of people on a watchlist and it's a lot harder.
Yes, that's what the US NIST was tasked with testing, e.g. for airports, border protection, etc. false alarm rates could easily get out of control and make a multi-million dollars systems useless. I've seen it in person 4 years ago at Dubai airport where a rack full of face recognition servers was permanently turned off. (I won't mention that vendor.)
Personally I'm only aware of NEC and Safran/Morpho having a solid track record in these kinds of large government contracts. Of course this is a somewhat controlled environment. A few months ago there was news that a police in UK was able to arrest a criminal through street surveillance. That was NEC, and that's why I became interested in trying it out myself.
Create New Topic
Christian Laforte
> the type of professional criminal that are often on such lists tend to be savvy about not staring up at potential cameras, welcome messages, etc.
You're right, detecting hardened criminals in the wild isn't full-proof yet and won't be for many more years. But I think there are other business opportunities that can live with less-than-perfect accuracy, e.g. retail.
Create New Topic
Undisclosed End User #1
Disclaimer, I worked for manufacturer for almost 10years that did Face Analytics and have cut my teeth in the school of Hard Knox, I once drank the Kool-Aide from the firehouse as well.
Welcome the world of Behavior Analytics in uncontrolled environments with uncooperative subjects. I suggest you not use "real-time alerting" as a knock-out requirement it will never work as planned w/o specially placed sensors and subjects who are in lane of analysis while looking in the direction of the sensor for a minimum of 2-3 seconds. Even with all of that there are so many variables.
Best value for FACIAL SURVEILLANCE is for forensic searching, you don't have real-time matching issues. You accept all of the false matches is search and weed them out but cannot do the same in real-time. Just like "G" Search for Best BrewPub, you take all the crap it gives you and filter out what you don't want to find the best cold beer, rank indexing. Take it for what it is, a SEARCH TOOL!
Only place where it works well enough is where there are controlled environments and subjects. Why does it work well in mantrap, well Johnny knows he must stare at the specially placed Face Camera until it matches him, he is a static subject and its easy for the algorithm to do its thing with less pixels and computing resources.
Why does other Face Recognition systems like those at DMV or pokey work well matching? Simple answer when you limit all of the variables then the analysis is much easier
Want a tip to beat Face Detection, look down when you walk in the area of analysis.
Create New Topic
Brandon Knutson
Fascinating concept! "Dubai Airport is replacing security checks with face-scanning fish"
Create New Topic