Role of Assembling Invariant Moments and SVM in Fingerprint Recognition

Fingerprint identification is one of the most well-known exposed biometrics, because of their uniqueness, distinctiveness and consistency over time. It is the method of identifying an individual and it can be used in various commercial, government and forensic 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 are allowed to 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. These 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.