KHOJ: Journal of Indian Management Research and Practices

Hemalata Chavan ¹, Aparna Joshi ¹

Affiliations
1 Pratibha College of Commerce and Computer Studies, Chinchwad, Pune-33, IN

Abstract

Biometrics is the process of automated recognition of individuals based on their behavioral and physiological characteristics. Physiological biometric traits include face and retina structures, fingerprint (whorls minutia), hand geometry, iris (pattern), ear (structure of the cartilaginous tissue of the pinna), palm vein and DNA structures etc.whereas Behavioral biometric traits include gait patterns, signatures, keystroke and odor etc. Unlike other identification methods such as id proof, tokens and password, the distinct aspect of biometric recognition comes into light from randomly distributed features in human being. Biometrics is one of the biggest tendencies in human identification. Biometric systems also introduce an aspect of user convenience; it means one can be authorized by representing himself or herself. Nowadays, biometrics is widely being used in many real life applications like security, forensic, and other identification and recognition purposes. In this paper, the main focus is on working principle of biometric technique, the various biometrics systems and their comparisons.

Keywords

Biometric System, Enrollment, Identification, Verification, Biometric Traits.

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Aparna Joshi ¹, Hemalata Chavan ¹

Affiliations
1 Pratibha College of Commerce & Computer Studies, Chinchwad, Pune, IN

Abstract

For optimum utilization of large volumes of image from multiple sources image fusion technique is applied. Image fusion is used for enhancement of the image for further applications. Image fusion seeks to combine information from multiple sources to achieve inferences that are not feasible from a single source. It should extract complete information from source images into the result, without introducing any artifacts or inconsistencies. The main aim of image fusion is spatial resolution enhancement or image sharpening for the purpose of human visual perception and computer processing. Fused data provides more robust aspects of operational performance such as increased confidence, reduced ambiguity, improved reliability and improved classification. Image fusion is also helpful to reduce the overall uncertainty and redundant information from various sources. There are various techniques used for image fusion which are classified at different levels such as pixel-level, feature-level and decision-level. It also classified on domain base such as spatial domain, transform domain and statistical domain (optimization approach).

Keywords

Image Fusion, Image Quality Metrics, Spatial Domain, Transform Domain.

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