DALTON Algorithm
Optimization of Swipe Fingerprint IC
FINGERPRINT IMAGE
CONVERT INTO FREQUENCY DOMAIN
ENCODE AND SAVE
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Extract traditional minutia instead of characteristic of fingerprint image
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Translate Data of Spatial Domain into Frequency Domain (Extract frequency)
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Fast matching-speed to comparison of code level
MUON Algorithm
Fast and Slim but Powerful !
FRR < 1.5% @ FAR 1:50,000 Time include image capture time and all of process inside algorithm
“image processing + extract features + matching”
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Support ultra small sized sensor (4mm x 4mm, 5mm x 2.8mm)
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More than 400 features are extracted and can be matched within 0.3 second
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Extracting abundant features from detailed ridge shape and pixel intensity change regardless of sensor size.
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All users with scar or discontinued ridges can enroll and verify without any problem unlike traditional minutia or pattern based algorithms
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No FPU, No math library are required !
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Optimized for TrustZone as well as embedded environment consuming small memory
CALISTO Algorithm
IRIS Recognition Algorithm
VS
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Iris recognition is a method of identifying people based on unique patterns within the ring-shaped region surrounding the pupil of the eye. The iris usually has a brown, blue, gray,
or greenish color, with complex patterns that are visible upon close inspection. Because it makes use of a biological characteristic, iris recognition is considered a form of biometric verification. -
Iris recognition is separated and extracted each different patterns through the algorithm, digitize them, and store them to identify users.
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Everyone's iris patterns are different.
even, the right and left iris of the same person are different. -
Noncontact recognition at the same time both eyes
