TOTAL SECURITY SOLUTION
SOLUTION
Competitive Edge of KJ TECH Face Reader
01
Enhanced Recognition
Speed and Accuracy
Powered by deep learning Al algorithms and achieving recognition speeds of 0.3-1 second with over 99.99% accuracy and significantly reduced False Acceptance Rate in diverse environments such as twin detection and different skin colors.
02
Environmental Stability
Equipped with infrared (IR) and 3D depth sensors ensures robust authentication performance regardless of ambient lighting
conditions or user appearance changes such as glasses, masks, hats etc.
03
Enhanced Security Features
Protected by advanced security features including 3D depth mapping, IR sensors, and liveness detection to prevent spoofing
attempts using photos, videos, or 3D models.
04
Improved User Experience
Featured with wide-angle lenses and Al-
based tracking for recognition during natural
movement and user-friendly display design for
intuitive authentication experience.
05
Advanced Data Processing and Privacy Protection
Protect privacy by storing only encrypted
biometric templates, not facial images
(compliant with international data protection
regulations including GDPR).
06
Multimodal
Authentication Support
Supports multiple authentication methods including
facial recognition, fingerprint, palm vein, QR code,
NFC/Bluetooth, PIN, and card authentication for
enhanced flexibility.
07
Installation and Maintenance
Supports easy installation with remote software upgrades and maintenance capabilities and self learning Al improves performance over time.
08
Cost Efficiency
Provides more affordable high-performance
solutions due to technological advances and
reduced operating costs through improved
energy efficiency.
09
Customization and Scalability
Supports expandable software and hardware
capabilities with OEM/ODM support for tailored
solutions meeting partner requirements.
Biometrics: The Science of Identity
Biometrics uses unique physical characteristics for authentication, offering significant advantages as a security solution:
01
Cannot be lost
02
Highly resistant to copying or theft
03
No need to carry additional items
These systems deliver unmatched uniqueness, accuracy, convenience, cost-effectiveness, and universal applicability.
Understanding Fingerprint Recognition
Fingerprints represent one of the most reliable biometric identifiers. While traditional forensic classification systems like the Henry System (categorizing prints as left loops, right loops, arches, whorls, and tented arches) remain important for law enforcement, modern biometric authentication relies on more detailed analysis.
Key Fingerprint Features
Modern fingerprint recognition technology focuses on distinctive points where ridge patterns break their flow:
- Minutiae: Points where ridges split, end, or form isolated features
- Core: The central point in fingerprint patterns (typically found in the inner axis of whorls, rings, or arches)
- Delta: Triangular ridge formations usually located at the lower corners of the print
Early fingerprint systems analyzed sweat pores along ridges, requiring increasingly higher resolution imaging technology for accurate capture.
Our advanced biometric readers leverage these unique characteristics to provide secure, reliable authentication systems for your organization.
Fingerprint
Human fingerprints consist of various types of options.
These fingerprints are classified based on decades of Henry System.
Henry System is a method of distinguishing features through classification criteria such as left loop (fingerprint with a line shape to the left), right loop, rch (semi-circular pattern), whorl, and tented arch (semi-circular curve like a tent).
Among the features of fingerprints, two-thirds are annular lines, one-third are swirls, and about 5%-10% are arched.
However, this classification is used only when a large amount of crime DB is used, but is not well used in fingerprint recognition (biometrics) technology.
Instead, in fingerprint recognition (biometric recognition), lines that interfere with the smooth flow of ridges become the basis data for most fingerprint recognition authentication.
According to a record of the features of fingerprints introduced by Galton in late 1800, most of them are at the point where the ridges are divided into two, the ridges at the beginning and end of the ridges. There are various minuteiae (characteristic point), which is dots (a very small melting point (a melting point seen as a point), islands (a melting point a little longer than the melting point), but a melting line (a melting line such as an isolated point), a pods or a split space (two spaces).
Human fingerprints consist of various types of options. These fingerprints are classified based on decades of Henry System. Henry System is a
method of distinguishing features through
classification criteria such as left loop
(fingerprint with a line shape to the left), right loop,
rch (semi-circular pattern), whorl, and tented arch
(semi-circular curve like a tent).
Among the features of fingerprints, two-thirds are
annular lines, one-third are swirls, and about
5%-10% are arched.
However, this classification is used only when a
large amount of crime DB is used, but is not well
used in fingerprint recognition (biometrics) technology. Instead, in fingerprint recognition
(biometric recognition), lines that interfere with the
smooth flow of ridges become the basis data for most fingerprint recognition authentication.
According to a record of the features of fingerprints introduced by Galton in late 1800, most of them are at the point where the ridges are divided into two, the ridges at the beginning and end of the ridges. There are various minuteiae (characteristic point), which is dots (a very small melting point (a melting point seen as a point), islands (a melting point a little longer than the melting point), but a melting line (a melting line such as an isolated point), a pods or a split space (two spaces).
Other features, such as fingerprint recognition, are very important elements, such as the core,
which is mainly the inner axis at the center of drawing vortexes, rings, or arches, and are mostly made at the ends of several curved ridges and ridges.
Delta is generally located at the bottom left or right of the fingerprint at the continuous end of a triangular ridge.
The ridges have fine sweatholes at a certain distance, and the initial attempts at fingerprint recognition used the location and
distribution of these sweatholes as data for authentication.
The resolution required to capture these sweatholes continues to increase.
Other features, such as fingerprint recognition, are
very important elements, such as the core,
which is mainly the inner axis at the center of
drawing vortexes, rings, or arches, and are mostly
made at the ends of several curved ridges and
ridges. Delta is generally located at the bottom left
or right of the fingerprint at the continuous end of a
triangular ridge. The ridges have fine sweatholes
at a certain distance, and the initial attempts at
fingerprint recognition used the location and
distribution of these sweatholes as data for
authentication. The resolution required to capture
these sweatholes continues to increase.
