Fingerprint Recognition Scheme using Assembling Invariant Moments and SVM

Fingerprint recognition is one of the most important Biometric techniques among all biometrics. It provides reliable means of biometric authentication due to its features Universality, Distinctiveness, Permanence and Accuracy. It is the method of identifying an individual and it can be used in various application, such as, medical records, criminal investigation, cloud computing communication etc. In cloud computing communications, information security involves the protection of information elements, only authorized users can access the available contents. However, traditional fingerprint recognition approaches have some demerits of easy losing rich information and poor performances due to the complex inputs, such as image rotation, incomplete input image, poor quality image enrollment, and so on. In order to overcome these shortcomings, a new fingerprint recognition scheme based on a set of assembled invariant moments i.e., Geometric moment and Zernike moment features are used to ensure the secure communications. This scheme is also based on an effective preprocessing, the extraction of local and global features and a powerful classification tool i.e. SVM (Support vector machine), thus it is able to handle the various input conditions encountered in the cloud computing communication. A SVM is used for matching the identification of test fingerprint inputs feature vectors with of the database images. The motivation behind the work is, growing need to identify a person for security. The fingerprint is one of the popular biometric methods used to authenticate human being. It is difficult to design accurate algorithms capable of extracting salient features and matching them in a robust way, especially in poor quality fingerprint images therefore, proposed fingerprint recognition provides reliable and better performance in poor quality images than the existing technique.