Fingerprints are composed of ridges and furrows arranged in different patterns making them distinct between each individual.  Fingerprints contain special features called “minutiae” that are extracted upon fingerprint enrollment and serves as personal identification badge.  

The minutiae are the points of interest or details in a fingerprint, such as bifurcations (a ridge splitting into two) and ridge endings.

Fingerprint Identification requires three components to complete the process: your fingerprint, the fingerprint recorder, and the database.  At the initial phase called “enrollment”, fingerprints are scanned, extracting the minutiae and transforms it into encrypted numerical data to be stored as “templates” in the database or written within a card.  Normally, a fingerprint recorder will require users to input their fingerprint thrice. This step allows the system to select the best quality among three attempts based on the predefined fingerprint classes of the system.  The fingerprint identification system categorizes the fingerprints into different classes depending upon the image quality.  The higher the class is, the better chance of getting the fingerprint accepted by the system.  After the enrollment of the first fingerprint, a substitute fingerprint is requested in case the first finger is injured or unable to recognize. 

Upon fingerprint authentication, the system matches the given minutiae of the fingerprint with the one stored within the database.  A matching score, similarity degree among 2 fingerprints, is computed.  The higher the score is, the higher is the degree of similarity between fingerprints.  The deciding factor that determines whether the fingerprint is accepted to be valid or not depends upon the threshold score set within the system.  Threshold is a score or a level that the matching score needs to reach or exceed in order for the fingerprint to be deemed as valid identity.  If the threshold score is high, the barrier for authenticating individuals is also elevated. 

The verification errors “False Acceptance Rate”(FAR) as well as “False Rejection Rate”(FRR) arises in relation with the threshold.  False Acceptance Rate is the rate of impostors accepted by the system while False Rejection Rate is the rate of valid users rejected by the system.