Fingerprint Classification

• Identification requires comparison of a person’s fingerprint with all fingerprints in a fingerprint database. Most of existing fingerprint datasets are very large (>1 million fingerprints). • In some cases other attributes such as race, gender, age, soft biometrics are introduces in the database, which allows partitioning of the dataset into smaller subsets. However, in most cases the data are fingerprints only. • Comparison of an on-line acquired fingerprint with all fingerprints in the database is computationally expensive (FBI dataset has >200 million fingerprints). • A solution to this problem is to divide the database into a number of bins (based on some predefined automatically extracted general fingerprint features). • Classification is referred to the assigning a class in consistent and reliable way. • Fingerprint matching is based on local features while fingerprint classification is based on global features, such as global ridge patter and singularities.