Identification and recovery of fingerprints from glass fragments in molotov cocktail cases

Increasing reports on Molotov cocktail cases in the local media has warrant a need for a detailed investigation of the perpetrator of the crime. A study is therefore embarked to compare fingerprint quality recovered from glass fragments of Molotov cocktails. The accelerants used were petrol, kerosene, diesel and motor oil. Different types of accelerant were used to observe the effect of accelerant on the quality of fingerprint recovered from glass fragment of Molotov cocktails. In the study, Molotov cocktails were exploded and glass fragments bearing fingerprint marks were collected and transported back to laboratory for analysis. Prior to fingerprint analysis, soot were removed from glass fragment using three techniques of brushing, NaOH (2 %) wash solution and tape lifting. After soot removal, enhancement fingerprint were done by using methods such as dusting method, superglue fuming method and Small Particle Reagent (SPR) method. Then, fingerprints from glass fragment of Molotov cocktails were identified by manual matching. Powder dusting method was used for sample petrol only because most of glass fragment were obtained in dry condition. Other than that, superglue fuming method was used in majority of sample whether Molotov cocktails were allowed to burn out naturally or the fire was extinguished using water. Small particle reagent method was mostly used for the wet glass fragment. Fingerprints recovered were photographed and were sent for manual matching. Based on the enhancement fingerprint method used, most of the latent fingerprint was developed with various qualities. Based on the percent recovery, SPR method shows the best recovery (43.75 %) at the scale 3 fingerprint, followed by superglue fuming and dusting powder. In manual matching method, percentage success rate in the case where fire of Molotov cocktails was allowed to burn out naturally was 55.56 % while in the case of fire extinguished using water, percentage success rate was 33.33 %. This study also showed that manual matching method of fingerprints recovered from Molotov cocktails with fingerprint obtained from suspect or standard can be done

Implementasi Alignment Point Pattern pada Sistem Pengenalan Sidik Jari Menggunakan Template Matching

Fingerprints is one of biometric identification system. This is because fingerprints have unique and different pattern in every human, so identification using fingerprints can no longer be doubted. But, manual fingerprint recognition by human hard to apply because of the complex pattern on it. Therefore, an accurate fingerprint matching system is needed. There are 3 steps needed for fingerprint recognition system, namely image enhancement, feature extraction, and matching. In this study, crossing number method is used as a minutiae extraction process and template matching is used for matching. We also add alignment point pattern  process added, which are ridge translation and  rotation to increase system performance. The system provide a performance of 18,54% with a matching process without alignment point pattern, and give performance of 67,40% by adding alignment point pattern process

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

Fingerprint Verification Using Spectral Minutiae Representations

Most fingerprint recognition systems are based on the use of a minutiae set, which is an unordered collection of minutiae locations and orientations suffering from various deformations such as translation, rotation, and scaling. The spectral minutiae representation introduced in this paper is a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require a fixed-length feature vector. This paper introduces the concept of algorithms for two representation methods: the location-based spectral minutiae representation and the orientation-based spectral minutiae representation. Both algorithms are evaluated using two correlation-based spectral minutiae matching algorithms. We present the performance of our algorithms on three fingerprint databases. We also show how the performance can be improved by using a fusion scheme and singular points

Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge

We propose a fully automatic minutiae extractor, called MinutiaeNet, based on deep neural networks with compact feature representation for fast comparison of minutiae sets. Specifically, first a network, called CoarseNet, estimates the minutiae score map and minutiae orientation based on convolutional neural network and fingerprint domain knowledge (enhanced image, orientation field, and segmentation map). Subsequently, another network, called FineNet, refines the candidate minutiae locations based on score map. We demonstrate the effectiveness of using the fingerprint domain knowledge together with the deep networks. Experimental results on both latent (NIST SD27) and plain (FVC 2004) public domain fingerprint datasets provide comprehensive empirical support for the merits of our method. Further, our method finds minutiae sets that are better in terms of precision and recall in comparison with state-of-the-art on these two datasets. Given the lack of annotated fingerprint datasets with minutiae ground truth, the proposed approach to robust minutiae detection will be useful to train network-based fingerprint matching algorithms as well as for evaluating fingerprint individuality at scale. MinutiaeNet is implemented in Tensorflow: https://github.com/luannd/MinutiaeNetComment: Accepted to International Conference on Biometrics (ICB 2018