Ahmed, are you looking for door entry, checkpoints, faces in a crowd? Will the subjects be cooperative (as in, look at the camera) or uncooperative (as in people walk by without even knowing the system is there)?
Reliability varies across these forms. Please clarify on that first so we can make better recommendations.
Speaking in general I wish these “governments” would hire their own scientists who could advise them on what the state of the art is in facial recognition and help them develop the applications of such that benefits them at a given cost. So much better than throwing out an RFP with unrealistic expectations.
Facial recognition is improving quite rapidly, and as it does it opens new opportunities for new applications. But it is still often counter intuitive in terms of its ability to satisfy certain requirements in many applications. One needs to weigh trade offs and that’s challenging without a deeper understanding of the subject, or when you’re evaluating a vendor’s sales pitch.
What is the application for Face? Are you simply trying to capture Faces for post forensic searching to see where faces have been or does application require real time alerting/marching? Big difference between Facial Capture and Facial Recognition in uncontrolled environment with uncooperative subjects.
Do you homework and don't oversell the solution, testing in a lab (controlled) vs real world is big difference.
- You have to do facial detection before facial recognition
- Facial recognition is not biometry (it match closest face according to a confidence level)
- Facial detection is well known using HaarCascade
Modern computer vision use DeepLearning instead of Cascade algorithm. This is very tricky because Deep Learning must be done in the cloud. Yann Lecun show some results on a laptop but it's not ready ...
I have tested the NEC NeoFace and Herta BioSurveillance sw. The NeoFace works very well. The cameras in my test : Axis 1MP, Bosch 5MP and Avigilon 1,2,3,5,16 MP. NeoFace is capabile to do recognition from live video, foto data base, recorded video files and snapshot from mobile app. The quality of database foto/live video must be + 70 (+100 ideal) pixels between the eyes. The customer has decided to go with Avigilon camera for a multisite project. The NEC sw is server to web cilent and multiple servers can be connected for speed. Foto data base 10k to 5000k. If you need more info, please contact me directly
If the reference foto is good quality 100+ pixels, like the passport id foto,then system will recognise the subject in following situation: sunglasses, wire rim and heavy frame glasses, eye close, with facial hair like mustache or not shaved, some type of head cover, +/- 5 ears aging, moderate facial scars and family related person like brothers or parents. For best performance is recommended that you store 3 photos of the subject in database. The accuracy is score with 0 to 1 and for a positive results score must be at least 0,5. For best results I recommend using cameras with 3+ MP in hallway, check points and 16+MP for wide FOV
John, Here at MorphoTrak, we are looking to achieve 60+ pixels Intra Eye Distance for reliable scanning in real time. Our customers in one transportation facility uses a real time scanning solution, which as Radu points out, is reliable even when the subject is wearing glasses. In another facility, we have had very good success when the subject is wearing a hat, so long as we have an "attractor" prompting the subject to look ahead while we capture the 'best frames" for analysis. I don't want to run afoul of the self-promotion rules, but I think that Ahmed might benefit from having more than one recommended vendor to compare against the use case. So, I would be willing to talk about real time scanning and post event processing of video with face recognition if that would be helpful.
We are starting a large middle eastern airport project this week, with both technologies.
Here is the problem I have with both of your descriptions. They are imprecise.
For example, Radu says "then system will recognise the subject in following situation". But it's never this absolute, unless Radu is saying that it is 100% in these conditions.
For anyone promoting their offering, let's get specific about percentage of top matches, conditions used and equally importantly how many total false matches one had to deal with in a given day / camera set. For example, if you have 1000 people on a watchlist, 1000 pass the camera a day, how many false alarms / mismatches will be displayed, etc.? Are either of you saying it's 0, that your systems never have any mismatches regardless of how many people walk by and that they always accurately match against subjects on the watchlist?
These things are important to understand how much the user needs to tolerate in terms of mistakes.