Latest Insight to Biometric Technology Innovations

According to a survey by Frost & Sullivan, Asia-Pacific biometrics market is rising to reach $21.2 billion by 2025, a remarkable jump from today’s estimation of $5.4 billion. This growth is due to the proliferation of biometric verification measures in many sectors for enhanced security and identification. A recent survey suggests that 81 percent of Americans said they are comfortable with using biometrics to confirm their identities in airports because of both convenience and safety from terrorism.

Biometric verification technology is being used in all the sectors for security purpose. This technology is also being deployed in mobile phones for identity verification purposes to deny the access of unauthorized persons. Biometric technology is coming up with many innovations to keep a tight reign on fraudsters and to combat digital frauds.

Latest Innovations of Biometric Technology:

Biometric technology is amazing for us with all new inventions every passing day. Recently, Hitachi launched a new biometrics system that leverages hand biometrics to verify users’ identities. It uses a sensor to trace the veins in users’ hands, then analyzes patterns in the veins and matches them to a database to confirm authenticity.

A new biometric tool called EarEcho has been developed to authenticate smartphone users via modified wireless earbuds that can detect individuals’ ear canals.

Airside Mobile has recently debuted a new digital ID service for the travel industry that leverages biometric authentication. AirsideX app by which users can upload passport information and then use facial recognition booths to match identities with stored information that the app deletes after confirmation.

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Biometric Verification through Face:

Biometric identification with the help of facial features means that not only the authenticity of a still image can be verified in real-time but in case of a live-feed, the system can check whether the person is actually present at the time of the verification and not a facial spoof or stolen still image is being used to either register for an online account or gain access to a secure account.

As one can imagine, unlike common methods of biometric authentication, face verification can be used to perform user validation even at remote places where the common biometric hardware cannot be supplied. Biometric verification has been around for enough years that businesses from all over the economic sphere have used it in one form or other but the inability of fingerprint scans or iris-based user verifications to operate in remote conditions was always a hindrance for businesses.

Although the introduction of fingerprint scanners and iris scanners in tablets, laptops and mobile phones was a welcome move in the implementation of biometric authentication. Unfortunately, the high cost of biometric induced devices has kept onboarding a problem, thus slashing the hopes of a universal biometric regime.

But face verification based biometric identification has opened a new avenue for user authentication which means that online businesses can leverage this technology to prevent online fraud and digital scams. Liveness detection is an accurate method to fight facial spoofing, something that is quickly becoming a medium to defraud online businesses.

Biometric Verification Opportunities

There are several opportunities that accompany a biometric identification system and it can be easily performed by a regular 2-D camera, in this case, a deep learning solution with a custom neural network is required. Here are a few of the opportunities explain in detail below

Image Quality Assessment

This solution is based on comparing the original image with a filtered image. One paper suggested that the differences between false images differ from real images and that they can be detected automatically. Features of image quality are the Mean Squared Errorf, Average Difference or Total Edge/Corner Difference. The next step is to send them to the classifier in order to determine if it’s a ‘real’ face or ‘fake’ face.

Image Distortion Analysis

Elements such as specular reflection, blurriness, chromatic moment and color diversity) can be sent for classification and placed into a classifier - trained for different types of spoofing attacks.

Deep Neural Network model

This is based on a model built with CNN (Convolutional Neural Network, which is the most popular neural network in image analysis). This aspect involves a cropped image of a face to pass into a neural network and then be processed through neural layers in order to classify as real or fake.