Enhancement of Latent Fingerprint Recognition Using Global Transform

Latent Fingerprints plays a vital role in identifying thefts, crime etc. Latent fingerprints are of 3 types. Noise in the Latent Fingerprints is removed by smoothing. Manual marking in Latent Fingerprint is slow and also latent examiner may make mistake while marking. The minutiae in the same latent marked by different latent examiners or even by the same examiner (but at different times) may not be the same. To overcome this issue new Orientation field estimation algorithm is introduced. It based on latent fingerprint feature extraction and edge detection. Orientation field estimation algorithm has dictionary construction stage. Dictionary Construction has 2 Stages. i) Offline stage ii) online stage. Orientation field estimation algorithm is applied for Overlapped fingerprint. Hough transform is used for detecting edges. It is shown that this method is slower to recognize latent fingerprint feature extraction and edge linking. In order to further increase the speed and perfect edge linking Hough transform method can be modified for better performance. Global transform is used for perfect edge linking and get the full fingerprint structure and comparison is made between two transforms to show which transform is better. DOI: 10.17762/ijritcc2321-8169.15034

Fingermark initial composition and aging using Fourier transform infrared microscopy (μ-FTIR)

This study investigated fingermark residues using Fourier transform infrared microscopy (μ- FTIR) in order to obtain fundamental information about the marks' initial composition and aging kinetics. This knowledge would be an asset for fundamental research on fingermarks, such as for dating purposes. Attenuated Total Reflection (ATR) and single-point reflection modes were tested on fresh fingermarks. ATR proved to be better suited and this mode was subsequently selected for further aging studies. Eccrine and sebaceous material was found in fresh and aged fingermarks and the spectral regions 1000-1850 cm-1 and 2700-3600 cm-1 were identified as the most informative. The impact of substrates (aluminium and glass slides) and storage conditions (storage in the light and in the dark) on fingermark aging was also studied. Chemometric analyses showed that fingermarks could be grouped according to their age regardless of the substrate when they were stored in an open box kept in an air-conditioned laboratory at around 20°C next to a window. On the contrary, when fingermarks were stored in the dark, only specimens deposited on the same substrate could be grouped by age. Thus, the substrate appeared to influence aging of fingermarks in the dark. Furthermore, PLS regression analyses were conducted in order to study the possibility of modelling fingermark aging for potential fingermark dating applications. The resulting models showed an overall precision of ±3 days and clearly demonstrated their capability to differentiate older fingermarks (20 and 34-days old) from newer ones (1, 3, 7 and 9-days old) regardless of the substrate and lighting conditions. These results are promising from a fingermark dating perspective. Further research is required to fully validate such models and assess their robustness and limitations in uncontrolled casework conditions

Authentication of the origin, variety and roasting degree of coffee samples by non-targeted HPLC-UV fingerprinting and chemometrics. Application to the detection and quantitation of adulterated coffee samples

In this work, non-targeted approaches relying on HPLC-UV chromatographic fingerprints were evaluated to address coffee characterization, classification, and authentication by chemometrics. In general, HPLC-UV fingerprints were good chemical descriptors for the classification of coffee samples by PLS-DA according to their country of origin, even for nearby countries such as Vietnam and Cambodia. Good classification was also observed according to the coffee variety (Arabica vs. Robusta) and the coffee roasting degree. Sample classification rates higher than 89.3% and 91.7% were obtained in all the evaluated cases for the PLS-DA calibrations and predictions, respectively. Besides, the coffee adulteration studies carried out by PLSR, and based on coffees adulterated with other production regions or variety, demonstrated the good capability of the proposed methodology for the detection and quantitation of the adulterant levels down to 15%. Calibration, cross-validation and prediction errors below 2.9, 6.5, and 8.9%, respectively, were obtained for most of the evaluated cases

HPLC fingerprints for the authentication of cranberry-based products based on multivariate calibration approaches

This work introduces the topic of the authentication of cranberry-based products and the detection and quantification of possible adulterations with other raw materials of lower quality. For such a purpose, genuine and adulterated cranberry samples were analyzed by reversed-phase HPLC with UV detection. Sample components were separated using an elution gradient based on 0.1% (v/v) formic acid aqueous solution and methanol as the components of the mobile phase. Chromatograms were recorded at 280, 370 and 520 nm. Data resulting from the injection of pure and adulterated samples, consisting of chromatographic fingerprints at each detection wavelength, were analyzed chemometrically. Preliminary studies by Principal Component Analysis showed that the sample extracts were clearly distributed depending on the extent of adulteration. Data was further treated by Partial Least Square regression to determine the percentages of grape contamination. It was found that even mixture samples containing low percentages of grape could be distinguished from genuine cranberry extracts. Besides, results obtained were highly satisfactory, with overall quantification errors lower than 5%. As a conclusion, the method proposed here resulted in an excellent approach to carry out the authentication of cranberry-based products relying on polyphenolic fingerprints

Characterization and Classification of Spanish Honey by Non-targeted LC-HRMS (Orbitrap) Fingerprinting and Multivariate Chemometric Methods

A non-targeted LC-HRMS fingerprinting methodology using an Orbitrap mass analyzer, and based on C18 reversed-phase mode under universal gradient elution, was developed to characterize and classify Spanish honey samples. A simple sample treatment consisting of honey dis-solution with water and a 1:1 dilution with methanol was proposed. 136 honey samples belonging to different blossom- and honeydew-honeys from different botanical varieties and produced in different Spanish geographical regions were analyzed. The obtained LC-HRMS fingerprints were employed as sample chemical descriptors for honey pattern recognition by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The results demonstrated a superior honey classification and discrimination capability with respect to previous non-targeted HPLC-UV fingerprinting approaches, being able to discriminate and authenticate the honey samples according to their botanical origins. Overall, noteworthy cross-validation multiclass predictions were accomplished, with sensitivity and specificity values higher than 96.2%, except for orange/lemon blossom (BL) and rosemary (RO) blossom-honeys. The proposed methodology was also able to classify and authenticate the climatic geographical production region of the analyzed honey samples, with cross-validation sensitivity and specificity values higher than 87.1%, and classification errors below 10.5%

Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach

The increasing availability of temporal network data is calling for more research on extracting and characterizing mesoscopic structures in temporal networks and on relating such structure to specific functions or properties of the system. An outstanding challenge is the extension of the results achieved for static networks to time-varying networks, where the topological structure of the system and the temporal activity patterns of its components are intertwined. Here we investigate the use of a latent factor decomposition technique, non-negative tensor factorization, to extract the community-activity structure of temporal networks. The method is intrinsically temporal and allows to simultaneously identify communities and to track their activity over time. We represent the time-varying adjacency matrix of a temporal network as a three-way tensor and approximate this tensor as a sum of terms that can be interpreted as communities of nodes with an associated activity time series. We summarize known computational techniques for tensor decomposition and discuss some quality metrics that can be used to tune the complexity of the factorized representation. We subsequently apply tensor factorization to a temporal network for which a ground truth is available for both the community structure and the temporal activity patterns. The data we use describe the social interactions of students in a school, the associations between students and school classes, and the spatio-temporal trajectories of students over time. We show that non-negative tensor factorization is capable of recovering the class structure with high accuracy. In particular, the extracted tensor components can be validated either as known school classes, or in terms of correlated activity patterns, i.e., of spatial and temporal coincidences that are determined by the known school activity schedule