Analysis of Fingerprint Image to Verify a Person

Identification and authentication technologies are increasing day by day to protect people and goods from crime and terrorism. This paper is aimed to discuss fingerprint technology in depth and analysis of fingerprint image. Verify a person with a highlight on fingerprint matching. Some fingerprint matching algorithms are analysed and compared. The outcomes of the analysis has identified some major issues or factors of fingerprinting, which are location, rotation, clipping, noise, non-linear distortion sensitiveness/ insensitiveness properties, computational cost and accuracy level of fingerprint matching algorithms. Also a new fingerprint matching algorithm proposed in this research work. The proposed algorithm has used Euclidean distance, angle difference, type as matching parameters instead of specific location parameter (like, x or y coordinates), which makes the algorithm location and rotation insensitive. The matching of local neighbourhoods at each stage makes the algorithm non-linear distortion insensitive

Multi-Modal Biometrics: Applications, Strategies and Operations

The need for adequate attention to security of lives and properties cannot be over-emphasised. Existing approaches to security management by various agencies and sectors have focused on the use of possession (card, token) and knowledge (password, username)-based strategies which are susceptible to forgetfulness, damage, loss, theft, forgery and other activities of fraudsters. The surest and most appropriate strategy for handling these challenges is the use of naturally endowed biometrics, which are the human physiological and behavioural characteristics. This paper presents an overview of the use of biometrics for human verification and identification. The applications, methodologies, operations, integration, fusion and strategies for multi-modal biometric systems that give more secured and reliable human identity management is also presented

Automatic methods for crop classification by merging satellite radar (sentinel 1) and optical (sentinel 2) . data and artificial intelligence analysis

Land use and land cover maps can support our understanding of coupled human- environment systems and provide important information for environmental modelling and water resource management. Satellite data are a valuable source for land use and land cover mapping. However, cloud-free or weather independent data are necessary to map cloud-prone regions. Merging radar with optical images would increase the accuracy of the study. Agricultural land cover is characterized by strong variations within relatively short time intervals. These dynamics are challenging for land cover classifications on the one hand, but deliver crucial information that can be used to improve the machine learning classifier’s performance on the other hand. A parcel-based map of the main crop classes of the Netherlands was produced implementing a script on GEE and using Copernicus data. The machine-learning model used is a Random Forest Classifier. This was done by combining time series of radar and multispectral images from Sentinel 1 and Sentinel 2 satellites, respectively. The results show the potential of providing useful information delivered by entirely open source data and uses a cloud computing-based approach. The algorithm combines the two satellites data of one year in a multibands image to feed in the classifier. Standard deviation and several vegetation indexes were added in order to have more variables for each 15-day-median image composite. The process paid particular attention to time variability of mean values of each field. This will provide useful information both for understanding differences among crops and variability over the phenology of the plant. The accuracy assessment demonstrates that several crop types (i.e. corn, tulip) can be better classified with both radar and optical images while others (i.e. sugar beet, barley) have an increased accuracy with only radar. The overall accuracy of RFC with optical and radar is 76% while it is 74% if only radar is used

Simple and secured access to networked home appliances via internet using SSL, BioHashing and single Authentication Server

This thesis describes a web-based application that will enable users to access their networked home appliances over the Internet in an easy, secured, accessible and cost effective manner, using the user's iris image only for authentication. As Internet is increasingly gaining significance and popularity in our daily lives, various home networking technologies also started gaining importance from consumers, which helped in facilitating interoperability, sharing of services and exchange of information between different electronic devices at home. As a result, the demand to be able to access home appliances or security cameras over the Internet gradually grew. In this research, we propose an efficient, secured, low-cost and user-friendly method to access networked home appliances over the Internet, providing strong, well integrated, three levels of security to the whole application and user data. According to our design, the user's iris data after hashing (using BioHashing) is sent through a secure communication channel utilizing Secure Sockets Layer v-3.0. The deterministic feature sequence from the iris image is extracted using 1D log-Gabor filters and while performing BioHashing, the orthonormalization of the pseudorandom number is implemented employing Gram-Schmidt orthonormalization algorithm. In addition to this protected data transfer mechanism, we propose the design of an Authentication Server that can be shared among multiple homes, allowing numerous users to access their home appliances in a trouble-free and secured manner. It can also bring down the cost of commercial realization of this endeavor and increase its accessibility without compromising on system security. We demonstrate that the recognition efficiency of this system is computationally effective with equal error rate (EER) of 0% and 6.75% (average) in two separate conditions on CASIA 1 and CASIA 2 iris image datasets

Raman spectroscopy: techniques and applications in the life sciences

Raman spectroscopy is an increasingly popular technique in many areas including biology and medicine. It is based on Raman scattering, a phenomenon in which incident photons lose or gain energy via interactions with vibrating molecules in a sample. These energy shifts can be used to obtain information regarding molecular composition of the sample with very high accuracy. Applications of Raman spectroscopy in the life sciences have included quantification of biomolecules, hyperspectral molecular imaging of cells and tissue, medical diagnosis, and others. This review briefly presents the physical origin of Raman scattering explaining the key classical and quantum mechanical concepts. Variations of the Raman effect will also be considered, including resonance, coherent, and enhanced Raman scattering. We discuss the molecular origins of prominent bands often found in the Raman spectra of biological samples. Finally, we examine several variations of Raman spectroscopy techniques in practice, looking at their applications, strengths, and challenges. This review is intended to be a starting resource for scientists new to Raman spectroscopy, providing theoretical background and practical examples as the foundation for further study and exploration