Visual Description

So this is a basic visual description of how we are conducting our experiment with the fingerprint data set. The diagram shows the template buckets. Each bucket represents each individual participating in the experiment, thus each bucket will have a template fingerprint scan and also a series of pruned fingerprint scan.



The diagram below depicts the score buckets for our results. We will test each fingerprint scan within the same bucket with the template (the normal finger scan) and record the score and put it into the genuine bucket. Then we will test a template from one bucket with various combination of scans from other buckets (so A.template will be tested with B.template, B.pruned1, B.pruned2, ..., H.template, H.pruned1,...) and the result of this should be put into the impostor bucket.



The histogram below shows an ideal histogram for the results. The curve on the right basically represents the genuine scores, and the curve on the left is the impostor scores. This is an ideal case however, so real results may vary.

Small Scale Histogram Representation

This week, we did a small scale histogram analysis of our fingerprint samples in MATLAB. In other word, we chose an image from our fingerprint dataset and plotted its histogram which is simply a tonal distribution of the image.

Fig1. Histogram Distribution

The image above is a graphical representation of tonal variations versus the number of pixels in that particular tone. Furthermore, using histogram equalization we looked at the intensity distribution of the original versus contrast enhanced image. The result corresponding to histogram equalization is displayed in Fig2,3, and 4.


Fig2. Original Image vs. Intensity Image



Fig3. Histogram Representation of the Original Image


Fig4. Histogram Representation of the Intensity Image

Sensor B in Action

This weekend, we were able to test Sensor B (generic TIR sensor). In comparison to our MSI sensor, the images are faded and not as significant. Below are some examples of the images





















So on the left is an enrollment of a fingerprint into its database. The sensor requires four different scans of the fingerprint. Then on the right is the verification process, which can be done recursively as many times as wanted. The panel on the right side will display whether the finger scan matched the enrolled fingerprint. Here you can see a number of VERIFIED messages, a few that did not register (the sensor did not sense a finger on the platen). And the last one is a failure (where the correct finger was placed but the sensor did not verify). The region that made contact on the platen was the tip of my finger.

TIR sensors are so picky....

There are certain conditions that TIR sensors require to get good quality images. These conditions are the following:

• Good contact between the finger and the platen
• Fingerprint features are well defined
• Skin has a proper index of refraction
• There is no water, oil or other contaminant on the platen
  

Below are some generic samples that were collected (using the fingerprint sensor described in Fingerprint Enhancement Using a Multispectral Sensor)


.

Further Analysis of TIR sensor technology

The source of light is generally from LED's (light emitting diodes) that are located on the left side of the prism. This light then reflects off of a diffuse reflective coating that is located on the right side, causing the upper region of the prism (the platen) to light up. If there is no finger placed on the platen, the light will reflect off the platen due to total internal reflectance. There is an imaging system in the lower left side, which will create the image of the top horizontal platen. So when a finger is placed on the platen, the light will cross the valley’s of the fingerprint and cause TIR at these “valley” like locations. So the image that is processed will have dark features where there is contact with the ridges, and lighter features where there is none.

Sensor A vs Sensor B

To further proceed with our research, we need to do a quick comparison and discuss the differences and similarities of two sensors.

Features of Sensor A

• Utilizes Multispectral imaging
• Flexible, powerful device outputs
• Compact design
• Pixel Resolution 500 dpi
  
• State-of-the-art prevention technology
• Expansive operating range

Features of Sensor B

• Utilizes optical fingerprint scanning technology
• Superior ESD resistance
• Small form factor
• Excellent image quality
• Encrypted fingerprint data
• Latent print rejection
• Counterfeit finger rejection
• Rotation invariant
• Rugged
• Works well with dry, moist, or rough
fingerprints
• Compatible with Windows® Vista,
XP Professional, 2000 and Windows
Server 2000, 2003

Key Specifications

• Pixel resolution: 512 dpi (average x, y
over the scan area)
• Scan capture area: 14.6 mm (nom.
width at center) 18.1 mm (nom.
length)
• 8-bit grayscale (256 levels of gray)
• Reader size (approximate): 79 mm x
49 mm x 19 mm
• Compatible with USB 1.0, 1.1 and 2.0
(Full Speed) specifications
• Indoor, home and office use

EUReKA

Every year, Jacobs School of Engineering gives undergraduate students the opportunity to showcase their research at Engineering Undergraduate Research Konference & Assembly (EUReKA). Karan and I decided to participate in this annual event by making a poster about our ongoing project especially since this year EUReKA will coincide with the Corporate Affiliates Program (CAP) board meeting in the Fung Auditorium, which could be an opportunity to get job offers from companies' representives presented at the CAP meeting. So this is a great opportunity to describe our research project, its implications, and results to-date.