Biometric Trends: What can we expect in the future?

The global biometric market is expected to double itself in value by the year 2025 according to recent forecast and analysis reports. Major reasons that lead to this expected growth are the increased usage of biometric technologies as part of various government initiatives, escalating need for security and surveillance due to terrorist attacks, consumer electronics, and booming of biometrics in the automobile and enterprise space. Biometrics has already proven to be a revolution in mobile technology. PINs and passwords are easily forgotten, lost, stolen and even hacked. Biometrics adds a layer of security where a person’s unique identity becomes their password, which makes devices, apps, and transactions more secure. 

With the surging demand for digital services such as mobile wallets, mobile banking, and other digital payments, the demand for cutting-edge user authentication is growing at a fast pace to ensure the security and convenience of customers. Based on the type of technology used, the biometrics technology market can be segmented into fingerprint recognition, palm recognition, face recognition, iris recognition, voice recognition, and others. 

Current trends in cutting-edge biometrics play a very important role in today’s online banking and e-government processes. Traditional methods of authentication like PINs and passwords are only based on what a person can memorize. Biometric traits are unique and personal, and cannot be easily lost or forgotten making it the most accurate way of identifying an individual. Fingerprint scanning is the most common technology used for biometric identification, with face and iris recognition not far behind. Though a majority of uses of biometric authentication are performed on-site, mobile device usage is growing rapidly. 

Voice printing is one such technology that is gaining popularity in recent times. A database of every citizen’s voiceprint is created. Frequency estimation, pattern matching algorithms, hidden Markov models, Gaussian mixture models, and neural networks are some of the techniques used to process and store voiceprints in this database. These voiceprints are then used as evidence when required during cases of kidnapping and other criminal activities. For example, when a kidnapper places a phone call demanding ransom, their voice can be matched and identified using this voiceprint database. Organized crime now focuses on data theft which puts our credit card details at risk of being compromised. Biometric authentication eliminates the risk brought about by hacking. 

When we use face recognition as an authentication method, we must admit to a fact that face duplication may occur in the case of twins, but their fingerprints will alw
ays be unique. The magnitude of instances where this can happen is meager at workplaces though. There can also be a few instances where the fingerprints of an individual are not available due to accidents or dirt. Face recognition then becomes a reliable option since facial features are always available in every individual. To get a face scanned, the person need not come in contact with any device or parts of it. This makes face recognition non-intrusive, unlike fingerprint scanning. Remote verification is one of the advantages of using a face recognition system. 

Face recognition systems can be made to work using just software but fingerprint enabled systems do require a combination of both hardware and software. This will further increase the cost of implementation. So, to implement facial recognition in a smartphone, all the manufacturer has to do is invest in good face recognition software. 

A possibility for spoofing a facial recognition system arises when the implementations are not capable of mapping the face in a 3-D configuration. In such cases, anybody can spoof the system by using another person’s photograph. Hence it is highly crucial to have updated versions of these devices to minimize false acceptance rates.