Fairytale from the Future: Facial Recognition

For most organizations, the ‘new normal’ is synonymous to ‘remote working.’ Remote working also means remote learning and upskilling being the need of the hour, learning is a vigorous process with us all. But what happens to assessment? Remote assessment is not something we are used to yet. Though there are some instances of remote assessment practices, but they are not fool proof thus not good enough for desperate time like these.

Traditionally, the authentication of the person appearing for the remote assessment was being done through designated username and password, and also two-factor authentication but there’s a loophole. It does not have proof of authentication. Thus, to ensure secured proctoring of these assessments, GCube proposes Facial Recognition, and we implore you that you hear us out as we shall be surely answering all the questions of high cost and accuracy issue.  

As a client posed the problem, we got into action to research the challenge and that is how we arrived at the facial recognition solution to make proctored assessment process seamless and efficient with practically zero imprecision. However, like with technology, facial recognition has been presented with challenges in its capacity but, GCube’s LMS has countered and resolved most of the following challenges by integrating a robust system.

High costs involved in facial recognition: Facial recognition involves continuous clicking and matching of the images with the ones in the database. Every passing second involves 24 frames, so a session of 30 minutes will increase the cost manifold. GCube’s solution counters these exorbitant costs by clicking the pictures of the learner at irregular intervals (which cannot be predicted by the learner) and matching it with the database. In addition, the FR now is offered as a service by providers like AWS and Google Vision so there’s no need to build a separate infrastructure. This way the costs are greatly controlled without affecting training and assessment.

Improved accuracy: Another challenge that used to come in the way of using facial recognition is accuracy. As the current facial recognition systems are trained on larger data the accuracy levels have increased following the very basic principle of machine learning. Of course, add to this the availability of high-end camera equipment at reasonable pricing which has improved the FR technology by many folds.

Privacy – the bone of contention in the field of FR:  For a long time, facial recognition has dealt with the issue of privacy. This has however been solved in the GCube facial recognition suite by matching with the reference image and erasing the rest of the captured data except in case of a discrepancy. In case of any discrepancy below the mismatch threshold of 70% between the uploaded photograph and the snapshot, the following error messages, dependent on the upload will be registered:

·             MF – Multiple Faces Detected

·             OF – One Face Detected

·             NF – No Face Detected

The snapshots which do not cross the mismatch threshold are stored for the proctor or mentor for their approval along with the original photographs the learners have uploaded. The proctor or the mentor can then access the report workflow and deal with them accordingly.

Thus, we have now made Facial Recognition an accessible technology and not a fairytale from the future.  

With over 100 industry awards in learning technologies, G-Cube would love to share more information with you about our facial recognition solution engineered specifically for the learning industry. Please do write to us here.

GCube’s BFSI LMS: Must have Mobile-First Features

 

According to a study by Mordor Intelligence, ‘The mobile learning market is expected to register a CAGR of 21.45% over the forecast period 2021-2026.’ With the pandemic moving people out of office, this was inevitable but our experience with some of the largest BFSI organizations in Asia Pacific, helped us realize this sooner. Not showing-off but we already did build a Mobile-first LMS for our clients long back.

While one of our clients needed a mobile-first LMS to enable faster onboarding of its 40,000 strong sales team; another banked on this technology to help in virtual sales. It is not just them but organizations that rely on mobile learning solutions saw a 16% boost in productivity and improvements in creativity and loyalty of their employees.

With reports of the global mobile workforce being set to increase to 1.87 billion people or 42.5% of the global workforce in 2022, up from 38.8% in 2016, workers have become increasingly mobile, which includes their primary work device. Now, it is important to understand that building a mobile version of a learning management system is not just changing the UI. It requires well-researched and thoughtfully applied features to ensure security and connectivity.

Here’s how we did it.   

Sensitive Content Security   

Mobile devices are personal devices with a public network connection. Thus, the content like internal data, processes, reports, fund values, customer information and even training manuals vulnerable to online abuse. To secure sensitive content on the device, the GCube BFSI LMS comes with Personal Information Encryption. Screenshot is disabled for all content.

Information Security Readiness

Security requirement in BFSI sector is much higher than in any other industry. So, the mobile LMS comes with multi-factor authorization for log-in. We also integrate with Mobile Device Management suites that secures your mobile LMS app for distribution withing your authorized employee base. This protects your app from external abuse or malicious activities. Additionally, it provides a mechanism of privacy for anybody who are accessing sensitive content on the app.

Customer Service & Sales Enablement

A very large section of the customer service and sales workforce in the BFSI sector is dispersed and works on the move. To enable them the Mobile LMS comes with ready guides for Complex Products and SOPs. The BFSI LMS has a very specific suite to also enable virtual sales.

Adept to Lesser Infrastructure

The dispersed workforce in the BFSI sector poses yet another challenge for technology access and that is lack of standard infrastructure. While in some cases the issue is of lower connectivity and in some other case, it’s a less sophisticated device. The LMS has been built to be conducive to these devices and makes content available in secure, downloadable format to tackle network problems.

For monitoring this dispersed and mobile workforce, we are developing a fleet management feature as well with geo-tracking and geo-fencing solutions.

With mobility and security at the heart of our R&D philosophy, GCube has won more than 100 awards for the learning technology innovations made over the last 20+ years. To know more about our solutions, please write to us.

Facial Recognition is not a Remote Assessment Fairytale Anymore!

Aviation is a dynamic industry and trust us, when we say that we understand the multi-layered issues of training and assessment in an industry where the workers are continuously on the move. In a set up that is so highly regularized and rightly so, training is not a casual upskilling exercise. The training in aviation is incomplete without the assessment and the authentication of the learner during this assessment which is remote in many occasions due to the dynamic nature of the industry.

Traditionally, the authentication of the person appearing for the remote assessment was being done through designated username and password, and also two-factor authentication but the loophole remains that it does not have proof of authentication. Thus, to ensure secured proctoring of these assessments, GCube proposes Facial Recognition, and we implore you that you hear us out as we shall be surely answering all the questions of high cost and accuracy issue.  

As a client posed the problem, we got into action to research the challenge and that is how we arrived at the facial recognition solution to make proctored assessment process seamless and efficient with practically zero imprecision. However, like with technology, facial recognition has been presented with challenges in its capacity but, the FR solution in GCube’s Aviation LMS has countered and resolved most of the following challenges by integrating a robust system.

High costs involved in facial recognition: Facial recognition involves continuous clicking and matching of the images with the ones in the database. Every passing second involves 24 frames, so a session of 30 minutes will increase the cost manifold. GCube’s solution counters these exorbitant costs by clicking the pictures of the learner at irregular intervals (which cannot be predicted by the learner) and matching it with the database. In addition, the FR now is offered as a service by providers like AWS and Google Vision so there’s no need to build a separate infrastructure. This way the costs are greatly controlled without affecting training and assessment.

Improved accuracy: Another challenge that used to come in the way of using facial recognition is accuracy. As the current facial recognition systems are trained on larger data the accuracy levels have increased following the very basic principle of machine learning. Of course, add to this the availability of high-end camera equipment at reasonable pricing which has improved the FR technology by many folds.

Privacy – the bone of contention in the field of FR:  For a long time, facial recognition has dealt with the issue of privacy. This has however been solved in the GCube facial recognition suite by matching with the reference image and erasing the rest of the captured data except in case of a discrepancy. In case of any discrepancy below the mismatch threshold of 70% between the uploaded photograph and the snapshot, the following error messages, dependent on the upload will be registered:

·             MF – Multiple Faces Detected

·             OF – One Face Detected

·             NF – No Face Detected

The snapshots which do not cross the mismatch threshold are stored for the proctor or mentor for their approval along with the original photographs the learners have uploaded. The proctor or the mentor can then access the report workflow and deal with them accordingly.

Thus, we have now made Facial Recognition an accessible technology and not a fairytale from the future.  

With over 100 industry awards in learning technologies, G-Cube would love to share more information with you about our facial recognition solution engineered specifically for the Aviation industry. Please do write to us here.