Survey On Content Base Information Retrieval Technique with Hybrid Nature

Authors: Divyata Patil, Rasna Sharma

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Abstract

Content-based information retrieval (CBIR) has been an active research area for the last two decades and much progress has been made in that time. However, there are still many challenges to be overcome and in this paper we highlight some of these together with some approaches that we have developed to address these problems. The content-based analysis of such dataset requires effective and efficient techniques for information extraction. In this paper describe the Biometric dataset for establishing the identity of an individual based on the physical, chemical or behavioral attributes of the person. On that biometric dataset we just apply information retrieval technique with hybrid nature. This technique is helpful in classifying the objects based on their extracted pattern. The presented paper introduces discussion about that technique. Keywords: CBIR, Biometric dataset, Feature extraction

Introduction

Biometrics is the science of establishing the identity of an individual based on the physical, chemical or behavioral attributes of the person. The relevance of biometrics in modern society has been reinforced by the need for largescale identity management systems whose functionality relies on the accurate determination of an individual’s identity in the context of several different applications. Examples of these applications include sharing networked computer resources, granting access to nuclear facilities, performing remote financial transactions or boarding a commercial flight. The proliferation of web-based services (e.g., online banking) and the deployment of decentralized customer service centers (e.g., credit cards) have further underscored the need for reliable identity management systems that can accommodate a large number of individuals. Biometrics offers certain advantages such as negative recognition and non repudiation that cannot be provided by tokens and passwords [32]. Negative recognition is the process by which a system determines that a certain individual is indeed enrolled in the system although the individual might deny it. This is especially critical in applications such as welfare disbursement where an impostor may attempt to claim multiple benefits (i.e., double dipping) under different names. Non-repudiation is a way to guarantee that an individual who accesses a certain facility cannot later deny using it (e.g., a person accesses a certain computer resource and later claims that an impostor must have used it under falsified credentials). Biometric systems use a variety of physical or behavioral characteristics including fingerprint, face, hand/finger geometry, iris, retina, signature, gait, palmprint, voice pattern, ear, hand vein, odor or the DNA information of an individual to establish identity [12, 36]. In the biometric literature, these characteristics are

Conclusion

The proposed work broadly focuses on the efficient storage and retrieval of some image-based biometric system based on physiological characteristics. These captured biometric images often contain noise and systematic variations due to different environments and user habit. Therefore, these datasets needs proper preprocessing before the application of data mining techniques. After the preprocessing step, the content descriptors of these biometric images need to be decided, and image features needs to be extracted and represented for effective retrieval of such large biometric databases. In order to achieve these requirements, intelligent methods may be incorporated in d