5 Ways to Perform Liveness Detection to Combat Bad Actors

Today’s digital world revolves around a delicate balance of information security and information access, this includes its management and the procedures that facilitate such to happen. Globally, institutions and businesses are vulnerable to the risk posed by fraud. In order to curtail and establish control, more technologically advanced solutions are being introduced like biometrics – fingerprints, facial, etc. Since biometrics are unique to each human individual, the use of them seems justifiable. However, even traditional biometric solutions have begun to face hurdles as more intuitive ways have been established to defeat such defenses. Regardless of what type of biometric it is.

Finding a Way Out – Spoof Detection

In order to develop a secure security system, it is imperative to understand that no single solutions can remain unchanged and stagnant. Where one single iteration is not enough to combat a constantly changing and evolving threats. Then why not the solutions as well? Keeping true to the spirit of innovation, it was necessary to improve existing solutions for increased effectiveness. Where re-inventing the wheel would not be necessary, just addressing the gaps in existing biometric solutions could solve many threats being faced by biometric solutions. The answer, introduce spoof detection procedures. Spoof detection involves procedures that aid in the determining of an individuals’ liveness or the presence of a real human.

Ensuring Real-Human Presence through these Ways

Intrinsic Facial Movement – Under this procedure, guided instructions are presented to an individual under facial verification. These instructions could be structured or appear to be, which might also be randomized to increase the probability of detecting a fraudulent actor. The individual would perform head movements as directed in front of a camera and this would be examined for correct follow-up.

Voice Acoustic Repetition – either visual or voice-guided instructions are presented to an individual under voice recognition. These instructions could be structured or appear to be, which might also be randomized to increase the probability of detecting a fraudulent actor. The individual would repeat the texts in the manner requested. The speech would be examined for patterns, this includes nasal tone, pitch, frequency, inflection, cadence. Basically, all the elements that establish a unique voice acoustic of an individual to establish genuineness.

Iris Movement – An eye scan is performed to detect the unique characteristics of an individual’s eye. Yes, like fingerprints each person has a unique set or irises as well. The patterns on the Iris are examined and established to perform checks of liveness to distinguish 2D scans from 3D ones in front of a camera. Color and pupil dilation by automated light changes are checked to ensure real-human presence.

Deep Neural Network – A deep machine learning solution that is trained to distinguish 2D imagery from a real human face. Its a constantly adaptive solution that examines all attempts made for verification and adapts to eliminate or flag attempts which have been previously carried out in a spoofing session.

Micro Expression Analysis – 2D imagery is flat, it does not exhibit changes in external skin texture. Whereas real humans are always in some ‘micromotion’. These are commonly referred to as micro expression that is checked for, which are only exhibited by real humans. Microexpression exhibit a degree of real-ness which is not found in flat two-dimensional imagery.

In order to combat fraud, it is imperative to implement solutions that exhibit a suitable level of security and fraud prevention. Incorporating biometrics with spoof detection is the direction forward to establish securer mobile platforms and solutions of tomorrow.

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