The identification of an individual using the analysis of segments from DNA.
The identification of an individual using the shape of the ear.
Eyes - Iris Recognition
The use of the features found in the iris to identify an individual.
Eyes - Retina Recognition
The use of patterns of veins in the back of the eye to accomplish recognition.
The analysis of facial features or patterns for the authentication or recognition of an individuals identity. Most face recognition systems either use eigenfaces or local feature analysis.
The use of the ridges and valleys (minutiae) found on the surface tips of a human finger to identify an individual.
Finger Geometry Recognition
The use of 3D geometry of the finger to determine identity.
The use of an individuals walking style or gait to determine identity.
Hand Geometry Recognition
The use of the geometric features of the hand such as the lengths of fingers and the width of the hand to identify an individual.
The use of an individuals odor to determine identity.
The use of the unique characteristics of a persons typing for establishing identity.
Vein recognition is a type of biometrics that can be used to identify individuals based on the vein patterns in the human finger or palm.
Voice - Speaker Identification
Identification is the task of determining an unknown speaker’s identity. Speaker identification is a 1:N (many) match where the voice is compared against N templates. Speaker identification systems can also be implemented covertly without the user’s knowledge to identify talkers in a discussion, alert automated systems of speaker changes, check if a user is already enrolled in a system, etc. For example, a police officer compares a sketch of an assailant against a database of previously documented criminals to find the closest match(es). In forensic applications, it is common to first perform a speaker identification process to create a list of “best matches” and then perform a series of verification processes to determine a conclusive match.
Voice - Speaker Verification/Authentication
The use of the voice as a method of determining the identity of a speaker for access control. If the speaker claims to be of a certain identity and the voice is used to verify this claim. Speaker verification is a 1:1 match where one speaker’s voice is matched to one template (also called a “voice print” or “voice model”). Speaker verification is usually employed as a “gatekeeper” in order to provide access to a secure system (e.g.: telephone banking). These systems operate with the user’s knowledge and typically require their cooperation. For example, presenting a person’s passport at border control is a verification process - the agent compares the person’s face to the picture in the document.
The authentication of an individual by the analysis of handwriting style, in particular the signature. There are two key types of digital handwritten signature authentication, Static and Dynamic. Static is most often a visual comparison between one scanned signature and another scanned signature, or a scanned signature against an ink signature. Technology is available to check two scanned signatures using advances algorithms. Dynamic is becoming more popular as ceremony data is captured along with the X,Y,T and P Coordinates of the signor from the signing device. This data can be utilised in a court of law using digital forensic examination tools, and to create a biometric template from which dynamic signatures can be authenticated either at time of signing or post signing, and as triggers in workflow processes.
Note: There is a difference between speaker recognition (recognising who is speaking) and speech recognition (recognising what is being said). These two terms are frequently confused, as is voice recognition. Voice recognition is a synonym for speaker, and thus not speech, recognition. In addition, there is a difference between the act of authentication (commonly referred to as speaker verification or speaker authentication) and identification.