Any individual seeking employment, licensing, or admittance into a college, university, or educational program may be needed to apply for a Fingerprint Clearance Card. A fortunate applicant will receive a small laminated card verifying that a person is capable of obtaining legal employment based on his criminal background or absence thereof.
Owners use the fingerprint clearance card as a means of setting up a potential or current employee’s background. Even though fingerprints do not change, a new set must be submitted to the Licensing Unit every time you renew a license to make sure no criminal activity has occurred because the last license was issued. For more details concerning the Fingerprint Clearance card process, see the Department of Public Safety.
Fingerprinting Recognition
Nowadays Fingerprint Recognition is a daily part of our lives; mobile phones, tablets, and even laptops now consider fingerprint recognition functionality standard. At work, more and more organizations are also using this type of biometric scanner to trail attendance and adjust their workforce alongside the security advantages it promotes, replacing passwords, ID cards, and door entry codes.
Easy usage
Quick, non-invasive, and simple to use, it’s essential to see why it has become the most developed and widely available biometric security remedy on the market. In case you are still unsure whether it’s worth the investment for your company, here are some advantages that might be useful to consider as you weigh up the available options.
How does Fingerprint Recognition function?
The program works by extracting meaningful attributes known as minutia points from the fingerprint. The detector picks out attributes such as orientation, change of ridge direction, arches, loops, and whorls in the print. Many scanners can even pick up pores on the skin. The programmed software then records and stores these minutia points to confirm the user’s identity in the future.
Five benefits of Fingerprint Recognition
Security – security-wise, it is a big improvement in passwords and recognition cards. Fingerprints in California are much harder to fake, they also change very little over a lifetime, so the data stays current for much longer than photos and passwords.
Ease of use – for the candidate they are simple and easy to utilize. No more fighting to remember your last password or being locked out due to leaving your photo ID at home. Your fingerprints will be always with you.
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Non-transferable – fingerprints are non-transferrable as is one of the best qualities, ruling out the sharing of passwords or ‘clocking in’ on behalf of another colleague. This enables more exact tracking of the workforce and renders additional security against the theft of sensitive materials.
Accountability – using fingerprint recognition also provides a higher level of accountability at work. Biometric evidence you have been present when a situation or incident has taken place is hard to refute and can be used as evidence if needed.
Cost-effective – from a digital management perspective, fingerprint identification is now a cost-effective security solution. Small hand-held detectors are easy to set up and benefit from a high level of veracity.
Fingerprint identification Process:
Generally, digital imaging technology is used in obtaining, storing, and analyzing fingerprint data.
Acquiring Images: As mentioned above, different scanners can be used to obtain fingerprint digital images. The fingerprint scanner comprises an optical scanner or a capacitance scanner. The optical scanner contains the charge-coupled device which includes light-sensitive diodes that give electric signals when removed.
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At the time you place your finger on a glass plate or monitor the surface, the camera captures the picture by illuminating the ridges of the finger. The remained image given below demonstrates the whole structure of the fingerprint acquisition using an optical detector and the right image is the real-time instance of the system.
Storing the pictures: The obtained image is then processed using the digital image processing method as explained below:
Image Segmentation: The obtained or captured image tends to contain unwanted features along with the relevant features. To eliminate this, thresholding based on the variance of each pixel in the image is done. The pixels having passion (Gray level value) greater than the threshold are considered while the pixels having intensity lesser than the threshold are removed.
Image Normalization: Each and every pixel in the image has a different mean variance. Thus, to obtain a uniform pattern, normalization is done, so that the image pixels are in the required range of gray values.
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Image Orientation: It indicates forming the image based on ridge orientation at each point. It is accomplished by calculating the gradient of each pixel in the x and y directions and then calculating the exposure by deciding the average of the vector orthogonal to the gradient.
Making the frequency image: It is completed to determine the local frequency (rate of occurrence) of ridges. It is executed by projecting the gray values of each pixel along with the direction vertical to the ridge orientation and then calculating the number of pixels between consecutive minimums in the waveform, which correspond to the ridges. The other way is using the Fourier transform method.
Image Filtering: It is finished to remove unnecessary noise. It is done either through a Gabor filter or a Butterworth filter. A standard way is folding the image with the filter.
Image Binarization: The refined image is then converted to a binary image using the thresholding method, to enhance the contrast. It relies on global thresholding, i.e., pixel value more than the threshold is set to 1, and pixel value less than, is set to 0.
Image thinning: It is finished to reduce foreground pixels until they are one pixel wide. It preserves the connectivity of the ridges.
Analyzing the Images: It involves extracting the minutiae details from the processed image and then comparing them with the already stored image patterns in the database. Minutiae extraction is done by calculating the crossing number or half of the sum of differences between pairs of pixels in an eight-linked neighborhood which is eight connected means a pixel surrounded by eight pixels. The cross number provides a unique recognition for each fingerprint in California.
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