Deep Learning Facial Recognition - Artificial Intelligence Meets IDV

The identity verification methods have modified in the past decades, while biometrics proved to be the most effective one. Biometrics recognition is the identification of a person using his unique biological and behavioral characteristics. In this world where smartphone penetration has captured every human being, there are very few who are unknown to biometric recognition. Now common users are using fingerprint and facial recognition for unlocking mobile phones instead of passwords and PINs.

Identification v/s Verification: Briefly Explained

Identification means authenticating the biometrics and verification means confirmation of the biometrics. For example, while signing up for a social media account, users set their password which is a part of authentication. After the account has been created, the users give their password which is confirmed with the previously stored data. This process is called verification. Similarly, with biometrics

Why Not Fingerprints, are they a thing of the Past?

Governments used to print fingerprints on identity documents. All the legal documents are signed and stamped by fingerprint marks. This creates proof of the person’s consent. When it comes to the digital world, fingerprints are used in building entrance and employee attendance systems.

A person just has to place his fingerprint biometrics on the scanner, and it records his time of entrance and attendance. This technology gained popularity because it eliminates manual record-keeping, creating a more normalized and standardized database. Fingerprint scanners are also used in the car doors unlocking.

Fingerprints are becoming less popular now because of its reasons mentioned below:

● High rate of false negative and false positive. A fingerprint scanner uses the fuzzy match approach which means that it will not 100% confirm the fingerprint but less than it. Some fraudster can verify himself using a prosthetic fingerprint
● Touching the scanner is mandatory. It is acceptable before the pandemic, but the Covid-19 SOPs have mandated less physical touch. Touching the same scanner by multiple people can spread the virus
● Expensive as it requires both hardware and software. Both of them need to be integrated
● Inefficient on dirty and damaged hands. People who do heavy manual work (by hands) have worn-out fingerprints. The registration of their fingerprints can be a difficult task

Due to the above-mentioned reasons, businesses shift towards other methods of biometric verification and the most suitable one is facial recognition.

Facial Recognition: A Way to Look in The Future

Facial recognition is widely used in this world. It fulfills all the loopholes of the fingerprint systems and that’s why it is much appreciated by businesses. The facial recognition market is forecasted to reach 8.5B US Dollars in 2025. Its usage and use cases are increasing over time.

For the identity verification industry, facial recognition is vital. It is used for varying the identities of humans in real-time. The live captured selfie is compared with the photo on the id card. The online facial recognition system performs the biometrics check using AI and deep learning algorithms.

Does Deep Learning Enhance the Security of facial Recognition, if yes then How?

From spoof attacks to deep fakes, a facial recognition system faces numerous types of faking attempts. Spotting these faking attempts can be a crucial task for a normal facial recognition software but for the one which uses deep learning algorithms.

Initially introduced by Facebook (named as DeepFace), it was utilized to identify human faces from digital images. It was trained on millions of images and incorporates a nine-layer neural network. FB climate to have more accuracy in facial recognition in some cases.

Deep learning can do extraction and classification at the same time without any human help. It has the ability of unsupervised learning from unstructured data given to it.

Wrapping It Up

Definitely, security has been enhanced in facial recognition system when integrated with deep learning technology. The primary addition to this is self-learning which gives better results when trained on new data sets. Businesses use this technology for authentication and verification, also transforming the old-school ways.

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