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Face Recognition for ID Authentication

Facial Recognition for ID Authentication

Recent developments inaccurate facial recognition technology, coupled with Artificial Intelligence(AI) algorithms, are being used for secure access control to enhance public safety. This newly emerging field of technology is called “Biometrics.”  Biometrics is collectively defined as the physical or behavioral personal characteristics that can be used to digitally confirm one’s identity, permit access to systems, devices, or information. There are different types of biometric identifiers such as facial recognition, fingerprints, voiceprint, DNA, a signature for ID authentication. Facial recognition applies AI imaging processing technologies by combining machine learning and computer vision to process large volumes of images easily and quickly. It also supports mask-wearing and face liveness detection to protect against spoof attacks (like with pictures) offering higher security and greater user experience than traditional authentication methods.

How does Face Recognition Work?

Biometrics use physical characteristics or behavioral characteristics to distinguish between multiple confirmed identities. Physical characteristics can be your face, fingerprints, iris pattern, and veins.  Behavioral characteristics can include your voice, handwriting, or keyboard typing rhythm. Your physical and behavioral characteristics clearly and inherently you so it is more difficult for someone to impersonate you when those elements are required for identity confirmation.  For these reasons, people consider them to be of a higher security level than regular keys or passwords.  Facial recognition would classify as a “physical characteristic” and we will go into more detail about detection methods later.

Face recognition, same as other biometrics systems, it uses the following steps for safe identity confirmation:Facial Recognition Technologies

Enrollment: This is the first step in the face recognition system that deals with capturing the basic information (face/image) used for specific characteristics in identity confirmation.

Storage: The aforementioned image is stored away.  Within the storage, the process is the analysis of the data and the conversion into a proprietary coding and-or graph.

Comparison: The next time you use the system, it compares the trait you present to the information on file. Then, it either accepts or rejects that you are who you claim to be.

Facial recognition is a form of biometrics that usually includes all the steps above for identity confirmation and contains all the components.  Facial recognition biometrics starts with a digital picture of someone’s face that is of adequate quality.  That picture cannot have a blurry appearance, be extremely small, or have any other things that can cause a false-positive reading.  That picture is stored, compared, and processed for identity confirmation using the same principles as given above.

What is Live Detection, Multi -AI Algorithms, and Its Advantages

Live detection (or also called “liveness detection”) is a phrase describing anti-spoofing technology that can be used to counter someone using a picture to impersonate you.  Due to the advent of increased spoofing using pictures on a smartphone or physically printed pictures, there is a greater need for accuracy in facial recognition. There are several ways this can be done with several different possibilities:

Texture analysis: this involves computing Local Binary Patterns (LBPs) over face regions and using an SVM to distinguish the faces as genuine or faked.

Frequency analysis: for example, it is done by examining the Fourier domain of the face.

Variable focusing analysis: such as checking the difference of pixel values between two successional frames.

Heuristic-based algorithms:  this algorithm can include eye movement, lip movement, and blink detection. The listed algorithms track eye movement and blink to confirm that the user is not holding up a photo of another person (since a printed image will not blink or move its lips).

Optical Flow algorithms: this particularly examines the differences and properties of optical flow created from 3D objects and 2D planes.

3D face shape: this item enables the face recognition system to discern between real faces and images of another person.

Combination: this is a strategy that can deploy a combination of the strategies listed above to create an optimized approach to facial recognition AI.

Besides the obvious, there are a plethora of advantages to using “live detection” with AI computing technologies as described in the following list:  higher software efficiency by eliminating false-positives increased product confidence due to higher levels of security.  Also, it is contactless, flexible, can provide a flexible personal audit-trail, interoperable with other security systems, and provides a uniquely simplified access control system.

LiDAR Sensor VS Camera for Face Recognition

LiDAR Sensors primarily do well at scanning flat surfaces, providing distance measurements, and tracking velocities. But imaging cameras can ensure that facial recognition systems distinguish between an actual three-dimensional (3D) face and a 2D video or photo for accurate and fast recognition. LiDAR is the use of a light-based sensor for measuring distances by shooting the targeted object with a laser and measuring the reflection with another part of the sensor. Time differences and wavelength differences (in laser return times and wavelengths) can then be used to make digital 3-D representations of the target. It has ground-based, air-based, and mobile-based uses. Digital cameras for facial recognition apply imaging processing technologies.  And they can detect with live recognition, the 3D facial scanning systems automatically encode the scanning images, and its imagery can be rapidly processed and compared by AI-based software.  Although LiDAR can graph and replicate 3D objects in a quick time, they lack the practical resolution necessary for rapid facial recognition.  Additionally, the accompanying software for some LiDAR sensors produces a very rudimentary rendering of certain physical characteristics when used at long range.

Facial Recognition Access Control Applications

Currently, accurate facial recognition systems are utilized in a plethora of applications from user logins with cameras, to law enforcement for ID identification, to collecting demographic datAccurate Face Recognition for Access Controla for business-related purposes, and for use in places where it’s necessary to identify VIP’s. For instance, security operations use fast facial recognition to control access to sensitive security areas of high risk.  Non-contact access control applications of facial recognition can be found in healthcare, food, banking, medical/pharmaceutical, schools, data centers, manufacturing plants, and gaming centers using facial recognition technologies. Various hardware and software platforms available enable the easier and wider applications of this new technology. Panel PC, touch screen, mobile devices including tablet pc, handheld tablets, and smartphones can be equipped with facial recognition technology, and it can be also operated in windows, Linux, and Android systems.

Additionally, the development of open-source software (like OpenCV or Tensorflow) and API docking have made it feasible for app developers or OEMs to quickly add related data into any app for mobile, desktop, or other device application for identity authentication.

To know more about our product with facial recognition applications, see our new product FSAC-80 on an 8” panel PC platform: a fever screen system equipped with imaging cameras (digital camera and thermal camera) for public safety and access control. Moreover, the product can be equipped with a card reader, so now you can have contactless authentication with RFID, NFC, CAC Card readers, and a display.  For more information, please visit our AI computing technologies.