Biometrics performs personal authentication using individual bodily features including fingerprints, faces, etc. These
technologies have been studied and developed for many years. In particular, fingerprint authentication has evolved over many years, and fingerprinting is currently one of world's most established biometric authentication techniques.
Not long ago this technique was only used for personal identification in criminal investigations and high-security
facilities. In recent years, however, various biometric authentication techniques have appeared in everyday applications.
Even though providing great convenience, they have also produced a number of technical issues concerning operation.
Generally, fingerprint authentication is comprised of a number of component technologies: (1) sensing technology for
detecting the fingerprint pattern; (2) image processing technology for converting the captured pattern into feature data
that can be used for verification; (3) verification technology for comparing the feature data with a reference and
determining whether it matches. Current fingerprint authentication issues, revealed in research results, originate with
fingerprint sensing technology. Sensing methods for detecting a person's fingerprint pattern for image processing are
particularly important because they impact overall fingerprint authentication performance. The following are the current
problems concerning sensing methods that occur in some cases: Some fingers whose fingerprints used to be difficult to
detect by conventional sensors. Fingerprint patterns are easily affected by the finger's surface condition, such noise as
discontinuities and thin spots can appear in fingerprint patterns obtained from wrinkled finger, sweaty finger, and so on.
To address these problems, we proposed a novel fingerprint sensor based on new scientific knowledge. A
characteristic of this new method is that obtained fingerprint patterns are not easily affected by the finger's surface
condition because it detects the fingerprint pattern inside the finger using transmitted light.
We examined optimization of illumination system of this novel fingerprint sensor to detect contrasty fingerprint pattern
from wide area and to improve image processing at (2).
We have developed an optical fingerprint sensor for personal identification. Conventional sensors detect contact between the convex parts of fingerprints and the input surface of the sensor, however, we have devised a novel sensor that utilizes the optical characteristics of the skin tissue under fingerprints. We obtained tomographic images from under fingerprints by optical coherence tomography (OCT), and discovered that the reflected and scattered light from the skin tissue under the concave parts of fingerprints was lower than the convex parts. In other words, the concave parts had a higher light transmittance than the convex parts. Moreover, even when there were wrinkles in a fingerprint, the same optical characteristics were present. Based on this, we made an experimental sensor that detected fingerprint patterns using light transmittance dispersion in the skin tissue. This sensor consists of light emitting diodes (LED) that irradiate red light from the side of a fingernail and an image formation system that forms an image onto an imaging device, by using the light that penetrated the finger. Using this sensor, we obtained fingerprint pattern images in which the concave parts were brighter than the convex parts. These results showed good agreement with the transmittance dispersion described above. Consequently, it has been demonstrated that a fingerprint sensor utilizing the optical can efficiently increase the recognition of fingerprint patterns of wrinkled or wet fingers, which conventional sensors have difficulty recognizing.