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Fingerprint recognition or fingerprint authentication refers to the automated method of verifying a match between two human fingerprints.Fingerprints are one of many forms of biometrics used to identify individuals and verify their identity. This article touches on two major classes of algorithms (minutia and pattern) and four sensor designs (optical, ultrasonic, passive capacitance, and active capacitance).Most of the current security and attendance systems are shifting towards automated biometric systems, the most popular biometrics being fingerprints. In Automated Fingerprint Identification Systems (AFIS), the fingerprint of an individual needs to be identified with that stored in the database. In this is a method which deals with fingerprint identification in the transform domain is considered and the main focus is on the reduction of the processing time. First, the mean of rows (or columns) of the fingerprint image is computed, this converts a two dimensional image signal into one dimension. The one dimensional Walsh transform of the row (or column) vector is generated and is distributed in a complex plane which is subjected to sectorization to generate the feature vector.The feature vector of a given test image is compared to those present in the database. The scores from row and column transform methods are fused using OR and MAX functions. The results with accuracy of more than 73% (for 16 sectors) and high computational speed show that the method can be used in fingerprint identification in application with requirements of less processing time.

The Human Development Index (HDI) is a relative measure of the country’s life expectancy, literacy, education, and living standards. It is a standard measure of wellbeing, especially of child welfare. The present study is an attempt to bring out the inter-district disparities in terms of human development in Maharashtra, applying the human development index method based on the optimal combination of selected human development indicators. Furthermore, to compare the levels of human development between the different districts through choropleth maps. The result shows that two districts are in the less developed category, i.e., Nandurbar and Gadchiroli, and rests of the 33 districts are included in the moderately high and very high human development group.