Well....holding steady and upward creep little by little as time...

Well....holding steady and upward creep little by little as time closes in...this post a bit long but maybe worth a read

Some good informative posts recently as usual by all like @redapple and a nice link by @naturalfibre informing all how the corneal tear film component interacts.

Been busy myself but still surfing as time permits and not a lot really to add last few days. Changed tack a little and went looking to see if I can find any "independent" info (to get comfy it is def viable) on visible light Iris evaluations / thoughts, as that obviously is the other key part of our new multi biometric.

I've covered b4 why NIR can be beneficial, though supposedly more expensive (and is used by Fujitsu, NTT Docomo etc) as it highlights Iris patterns clearly, even dark Irides however as a by product reduces corneal specular reflections (which we use for the corneal component) so visible light is better for us but dark Irides can have issues previously with definition.

So...visible light works basically works in RGB spectrum (Red, Green & Blue) and I have just found a recent study / analysis paper out of Poland from Aug 2015 at the joint IEEE - SPIE Symposium around precisely (IMO) what we want to achieve with RGB, smart phone flashes and Iris identification, Corneal reflections etc. They believe it could be the first comprehensive analysis of this concept....I guess maybe in the public forum anyway  

Link at end of post and a couple of key sections cut / paste FYI to abbreviate it all...abstract...grey scaling via the Red channel (colour iris pics to grey scale comparison - check dark iride) and conclusion.

Must also be read in knowledge that this group have only used Iphone 5s and Lumia 1020 phones with purely currently COTS (commercially of the shelf) available Iris recognition methods (algos essentially I believe) in Mirlin & VeriEye for their testing methodology.

Their early results are quite acceptable IMO from the equip they had available but overall, for me, it just further supports the path and tech that WBT are currently testing as being highly viable in achieving what they need to....will see soon on first pass or whether needs a couple of tweaks yet but looks good to me so far

I posted a guess a couple mths back that grey scaling may be an option...waiting to see the tech specs from WBT

Nice eve all - highlighted a few bits but note the last sentence - that is wot WBT are looking to achieve (with the added corneal biometric & inbuilt liveness & one time pin) IMO.

ABSTRACT
In the age of modern, hyperconnected society that increasingly relies on mobile devices and solutions, implementing a reliable and accurate biometric system employing iris recognition presents new challenges. Typical biometric systems employing iris analysis require expensive and complicated hardware. We therefore explore an alternative way using visible spectrum iris imaging.

This paper aims at answering several questions related to applying iris biometrics for images obtained in
the visible spectrum using smartphone camera. Can irides be successfully and effortlessly imaged using a smartphone’s built-in camera? Can existing iris recognition methods perform well when presented with such images? The main advantage of using near-infrared (NIR) illumination in dedicated iris recognition cameras is good performance almost independent of the iris color and pigmentation. Are the images obtained from smartphone’s camera of sufficient quality even for the dark irides?

We present experiments incorporating simple image preprocessing to find the best visibility of iris texture, followed by a performance study to assess whether iris recognition methods originally aimed at NIR iris images perform well with visible light images. To our best knowledge this is the first comprehensive analysis of iris recognition performance using a database of high-quality images collected in visible light using the smartphones flashlight together with the application of commercial off-the-shelf (COTS) iris recognition methods.



5. CONCLUSIONS AND FUTURE WORK
The experiments regarding iris recognition performance conducted in this study managed to provide insight concerning several interesting issues. We have shown that it is perfectly viable to employ a smartphone’s camera (provided it is of good quality) for the purpose of iris pattern imaging. Images we acquired are of good quality, and with appropriate image preprocessing allowed us to use two commercially available iris recognition methods with low error rates. The enrollment performance is good, with sample rejection rates of about 1% (yet we managed to even achieve 0% FTE with the VeriEye method and session 1 dataset). We may thus hazard a guess that the enrollment stage is barely affected by the fact that different type of data is deployed to the algorithm instead of typical NIR samples.

We have also managed to get satisfactory results in the matching stage (actually comparing the samples). With FNMR @Zero FMR value of a little more than 2% for the MIRLIN method we could expect false rejecting two samples in a hundred, with no false accepted samples, which can be considered a good performance.

Moreover, the MIRLIN method yields better results than the competing VeriEye algorithm, which may
suggest that it employs technology that is less susceptible to different types of input data, however, the
fact that VeriEye’s manufacturer does not enable access to the localization results, it is hard to know the
underlying reasons of inferior performance given by their method. However, for the MIRLIN matcher such possibility exists, and visual inspection of the samples producing exceptionally bad similarity scores revealed that in most cases the erroneous localization is to blame, and therefore, the encoding and comparing the samples work well most of the time, and it is the image segmentation that needs improvement.

In future work we hope to develop a method that would allow reliable localization of the eye region and
automatic cropping of the images to the desired format. This would in turn enable running experiments that would more accurately mimic a real-world deployment scenario and provide more insight on the recognition accuracy and system performance that can actually be achieved. With more data and research we will be able to get closer to answering the fundamental question, that is if we can fulfill the promise of effortless, inexpensive and reliable iris recognition, that can be deployed on any device or platform.

https://www.google.com.au/url?sa=t&...camera&usg=AFQjCNGJO_9gKhP1FvwLJEbLBvrROyXXDw