Digital Image Transformations and Image Stacking of Latent Prints Processed Using Multiple Physical and Chemical Techniques

Latent fingerprints are highly common among evidence that is found at crime scenes. While fingerprint evidence can be very reliable, comparison and identification of a print is highly affected by the quality of the fingerprint image. Fingerprint experts ideally want to have an image with the best quality possible, in order to make an accurate identification and avoid missing pertinent details. This is a thesis presented on the use of digital image processing to merge multiple images of one fingerprint to obtain a final image with greater quality. The research was conducted using different latent fingerprint processing techniques that have been widely used in the forensic science community: ninhydrin, DFO, zinc chloride, cyanoacrylate, and fluorescent dye-stains. The latents were photographed after each technique was utilized. Images of the same print under different wavelengths and filters were merged to create a final image with ideally better contrast, quality, and friction ridge detail than were observed in the original images prior to merging. Quality was determined using three different scoring methods; NFIQ, Bandey scale, and AFIX Tracker. A print was considered to be improved if the merged score was better than the scores of the original images. There were 12.1 % of prints that were improved based on NFIQ scores, 2.8 % based on Bandey scores, and 15.0 % based on AFIX match scores. Image fusion for increasing quality of latent fingerprint images is a method that shows small benefits for the examiner when performing a comparison

Scope analysis of different kinds of fingerprint

As Biometrics are the most widely used technique for person identification and verificationwith different applications, this research paper is mainly focused on the different kinds ofFingerprints based on availability and acquisition process. Fingerprints can broadly becategorized into three categories: Live Scan, Latent, and Patent Fingerprints. The mainobjective of this research is to present importance of different kinds of fingerprint with theirrecognition techniques. The quality of fingerprint image also plays an important role duringthe recognition process, because it requires extra time to improve quality of image. Thereare different types of image enhancement methods are available applicable differently ondifferent kinds of fingerprint images. This research provides basic information aboutfingerprints to the research direction

Polarization- and Specular-Reflection-Based, Non-contact Latent Fingerprint Imaging and Lifting

In forensic science the finger marks left unintentionally by people at a crime scene are referred to as latent fingerprints . Most existing techniques to detect and lift latent fingerprints require application of certain material directly onto the exhibit. The chemical and physical processing applied onto the fingerprint potentially degrades or prevents further forensic testing on the same evidence sample. Many existing methods also come with deleterious side effects. We introduce a method to detect and extract latent fingerprint images without applying any powder or chemicals on the object. Our method is based on the optical phenomena of polarization and specular reflection together with the physiology of fingerprint formation. The recovered image quality is comparable to existing methods. In some cases like the sticky side of a tape our method shows unique advantages

Investigation And Development Of Flattening Algorithms For Curved Latent Fingerprint Images

Fingerprint had been used to identify a person due to its uniqueness and unchangeable throughout life. However, latent fingerprint acquisition normally being performed on uneven or noisy surface with poor contrast, causing fingerprint minutiae point extracted appear to be inaccurate and affect the result of fingerprint matching. Thus, latent fingerprint required image to be pre-process and enhance before latent search. In order to increase latent matching accuracy, geometry rectification is needed to correct distortion in fingerprint images due to uneven surfaces. This research will investigate and develop flattening algorithm that can be adapted to latent fingerprint images on cylindrical surface. The boundary of an image is required to detect the curvature of an image that need to be flattened. Boundary of interested area can be acquired using a predefined algorithm or define by user using interactive drawing. The flattening algorithm required mapping from cylindrical coordinate to image coordinate. Since curved image appears to be rectangular shape, parabolic approximation and ellipse approximation are being used to design algorithms for flattening. Experimental results prove that algorithm that applies ellipse equation to flatten fingerprint images able to increase the quality of the minutiae. However, measurement results for horizontal axes shows that the distortion in horizontal axis is not being well taken care of. In summary, both algorithms developed able to flatten curved latent fingerprint images with the assumption that image that needs to be flattened is vertical cylindrical shape and boundary of cylinder must be detectable. Algorithm that applies ellipse approximation provides better performance as compared with the algorithm that developed based on parabolic approximation

Image enhancement and segmentation on simultaneous latent fingerprint detection

A simultaneous latent fingerprint (SLF) image consists of multi-print of individual fingerprints that is lifted from a surface, typically at the crime scenes. Due to the nature and the poor quality of latent fingerprint image, segmentation becomes an important and very challenging task. This thesis presents an algorithm to segment individual fingerprints for SLF image. The algorithm aim to separate the fingerprint region of interest from image background, which identifies the distal phalanx portion of each finger that appears in SLF image. The algorithm utilizes ridge orientation and frequency features based on block-wise pixels. A combination of Gabor Filter and Fourier transform is implemented in the normalization stage. In the pre-processing stage, a modified version of Histogram equalization is proposed known as Alteration Histogram Equalization (AltHE). Sliding windows are applied to create bounding boxes in order to find out the distal phalanges region at the segmentation stage. To verify the capability of the proposed segmentation algorithm, the segmentation results is evaluated in two aspects: a comparison with the ground truth foreground and matching performance based on segmented region. The ground truth foreground refers to the manual mark up region of interest area. In order to evaluate the performance of this method, experiments are performed on the Indian Institute of Information Technology Database- Simultaneous Latent Fingerprint (IIITD-SLF). Using the proposed algorithm, the segmented images were supplied as the input image for the matching process via a state art of matcher, VeriFinger SDK. Segmentation of 240 images is performed and compared with manual segmentation methods. The results show that the proposed algorithm achieves a correct segmentation of 77.5% of the SLF images under test