A minutia features generation apparatus comprises: an input part to input an image formed as a curved stripe pattern by a ridge line(s); a generation part to generate a skeleton image formed by extracting a skeleton(s) from the image; an extraction part to extract a plurality of minutiae from the skeleton image; and a calculation part configured to calculate a relation minutia feature(s) representing relationship between a first minutia and a second minutia among the plurality of minutiae, wherein the calculation part calculates as one of the relation minutia features defined by a crossing count of the skeleton(s) and a straight line connecting from the second minutia to a nearest neighbor point which is a point on a trace line traced by tracing starting from the first minutia, which point is located at a shortest line distance from the second minutia.

Some disclosed methods involve receiving, by a control system, touch sensor signals from a touch sensor system indicating a touch of a target object in a target object touch area and controlling, by the control system and responsive to the touch sensor signals, a plurality of display pixels of a display stack to illuminate the target object touch area. Such methods may involve receiving, by the control system and from an optical sensor system, optical sensor signals corresponding to light transmitted from the plurality of display pixels, reflected or scattered from the target object, transmitted through a plurality of display stack apertures, directed by a light guide system and received by an optical sensor system. Such methods may involve performing, by the control system, an authentication process based, at least in part, on the optical sensor signals.

Provided is a method for determining a match between a candidate fingerprint and a reference fingerprint characterized by minutiae local features. The method includes evaluating a similarity of the candidate fingerprint local feature and the reference fingerprint local feature of a current local feature pair, and determining a match depending on the similarity evaluation and geometric coherence evaluations performed for said current local feature pair. Other embodiments are disclosed.

Provided is a method for determining a match between a candidate fingerprint and a reference fingerprint characterized by minutiae local features. The method includes extracting several minutiae from the candidate fingerprint, computing from said extracted minutiae a plurality of minutiae local features of the candidate fingerprint, computing a first global matching score between the candidate fingerprint and the reference fingerprint based on the first similarity scores of said matching local feature pairs; computing a second global matching score between the candidate fingerprint and the reference fingerprint based on said computed second similarity scores; and determining a match between the candidate fingerprint and the reference fingerprint comprising: comparing the first and second global matching scores and, comparing the first matching score to a matching threshold. Other embodiments disclosed.

Methods, systems, and programming for identifying a person are presented. In one example, a first set of candidates is determined based on a fingerprint image acquired from a finger of the person. A second set of candidates is determined based on a finger vein image acquired from the finger. The person is recognized based on the first and/or second set of candidates.

There is provided an information processing apparatus and an information processing method capable of improving security against leakage of a registered fingerprint in fingerprint authentication. Collation is performed on the basis of whether or not a feature amount of a partial region of an entire fingerprint imaged by a fingerprint imaging unit and a method of operation on the fingerprint imaging unit match the feature amount of the partial region of the entire fingerprint and the method of operation registered in advance. Application to a fingerprint authentication device is possible.

A method carried out by a smart card including a fingerprint sensor, the method including an authentication phase including: acquiring a sequence of object prints from objects detected over time by the fingerprint sensor; comparing the sequence of object prints with reference data defining a reference sequence of object prints according to at least two different print types; and determining that the authentication phase is successful if the sequence of object prints coincides with the reference data. During a digital enrollment phase, the smart card generates, from at least one fingerprint, called enrollment fingerprint, digital enrollment data; and records these digital enrollment data so as to allow subsequent digital authentication from said digital enrollment data.

Techniques for client side multi-factor password generation include randomly removing one or more features of a record of a fingerprint image of a user and creating a distorted record of the fingerprint image by merging the record with a user input code using an encryption technique, the distorted record being reversible using the user input code. The distorted record for authentication of the user is registered.

A mobile device comprising memory configured to store instructions; and a processor, the instructions configured to cause the processor to: capture small specimens of livescan fingerprint ridges using a scaled down fingerprint sensor, use RSM matching to find the ridge structure in a matching reference, use Level 3 Features to confirm the livescan-to-reference match, and use Level 3 features selectively based on “guidance” provided using Level 2 features.

A method for controlling unlocking and a terminal includes the follows. Feature points of class i of a finger of a user is acquired by scanning the finger with the i.sup.th scanning partition of a fingerprint recognition sensor of a terminal, the fingerprint recognition sensor has M scanning partitions, M is a positive integer not less than 2 and i is a positive integer not greater than M. The acquired feature points of class i is added into a current feature point set. A matching process is performed between the current feature point set and a pre-stored fingerprint template. The terminal is unlocked when the current feature point set is matched.