Marina Gavrilova. Scientific question: If a person's photo in the system's database was taken 10 years ago, is it possible to identify the person today?

Marina Gavrilova

Scientific question:

If a person's photo in the system's database was taken 10 years ago, is it possible to

identify the person today?

Answer can be provided by next generation face reconstruction engines.

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Fusion

Biometric Fusion means integration of biometric information.

The goal of fusion scheme is to devise an appropriate

function that can optimally combine the information

rendered by the biometric subsystems.

Broadly classified as –

Prior-to-matching fusion and After-matching fusion

Humanoid robots are anthropomorphic robots (have human-like shape)

that include also human-like behavioral traits. The field of humanoid

robotics includes various challenging direct and inverse biometrics.

On the other hand, in relation to inverse biometrics, robots attempt

to generate postures, poses, face expressions to better communicate

their human masters (or to each other) the internal states [49]).Robots such as Kismet express calm, interest, disgust,

happiness,surprise, etc (see (MIT projects).

More advanced aspects include dialogue and logical reasoning similar to those of humans. As more robots would enter our society it will become useful to distinguish them among each other by robotic biometrics.

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More advanced aspects include dialogue and

logical reasoning similar to those of humans.

As more robots would enter our society it will become useful to distinguish them among each other by robotic biometrics.

Asimo (Honda) humanoid robot

http://en.wikipedia.org/wiki/ASIMO

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Email ◦ Changed the way we communicate in today’s

highly technical world What's wrong with the app of the Internet?

◦ Hard to know who sent an email◦ Spam

Unsolicited email Offensive Fraudulent (phishing) Malicious (viruses, worms, spyware, exploits, DoS)

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Email (E)

Signing AlgorithmPrivate key D

Signature (D(H(E)))

Public key E

If H`(E) = H(E) then message is authentic

E

Hash H (MD5, SHA-1, etc)

D(H(E)) Hash H

H`(E) H(E)

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PEM (Privacy Enhanced Mail) [3] – mid 1980◦ ASCII email messages◦ No centralized public key directory◦ Single root to issue CAs (Certificate Authority)

S/MIME (Secure Multipurpose Internet Mail Extension) [4] ◦ Accommodate any number of trusted CAs

PGP (Pretty Good Privacy) [5]◦ Web of trust◦ Widely used

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Garfinkel [6] presented a new approach to solve most of the usability issues

Used only for encryption Outgoing email

◦ Looks up users public keys in a local database◦ Appends the user’s public key to the email header

Incoming email◦ Stores public keys found in the email header

Vulnerable to man-in-the-middle attacks

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Brown and Snow [7] presented a similar approach but adds digital signatures

Proxy-based approach sitting between the mail client and mail server

Encrypts and signs all outgoing emails Decrypts and verifies all incoming emails

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Idea◦ Use fingerprints instead of private keys

Primary goals◦ Secure access to email accounts◦ Provide an easier way to sign and verify emails◦ Solve the usability issues

Implemented as an email client called SEFR SEFR asks you to present your fingerprint

◦ When you access it and try to view your inbox◦ When you try to send an email

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Components◦ Database: used to store user’s fingerprints and

account information dbs2.cpsc.ucalgary.ca

◦ Enroller: used to enroll new users◦ Receiver: used to download the user’s inbox

Using POP (Post Office Protocol) [1] Gmail’s POP server – pop.gmail.com Port 995

◦ Sender: used to send emails Using SMTP (Simple Mail Transfer Protocol) [2] Gmail’s SMTP server – smtp.gmail.com Port 465

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Accounts on Gmail◦ Two accounts were created for testing and

experimentation purposes amaobied sefr.obied

Issues◦ Gmail servers requires the use of SSL

OpenSSL Base 64 encoding

◦ Fingerprint scanner in the BT lab No API Used fingerprint image paths

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Signing messages◦ When a user tries to send an email, SEFR asks the

user to present his/her fingerprint. If the fingerprint is stored in the database, SEFR does the following: Transforms the email message (e.g., get rid of

newlines, tabs, spaces, etc) Create a hash using SHA-1 of the transformed

message Store the sender’s email address, receiver’s email

address and hash in the database

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Verifying messages◦ When SEFR tries to verify an email, SEFR

automatically does the following: Transforms the messages (e.g., get rid of newlines,

tabs, spaces, etc) Creates a hash using SHA-1 of the transformed

message Extracts the sender’s email address, receiver’s email

address from the email header Checks if the sender’s email address is associated

with the receiver’s email address and hash value in the database

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Using biometric authentication to access Web-based system◦ Online banking

Defeating Spam◦ Bill Gates said “Two years from now, spam will be

solved”

The issue of protecting privacy in biometric systems has inspired the area of so-called cancelable biometrics. It was first initiated by The Exploratory Computer Vision Group at IBM T.J. Watson Research Center and published in [2].

Cancelable biometrics aim to enhance the security and privacy of biometric authentication through generation of “deformed“ biometric data, i.e. synthetic biometrics.

Instead of using a true object (finger, face), thefingerprint or face image is intentionally distorted in a

repeatable manner, and this new print or image is used.

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The features of the new generation of lie detectors include:

(a) Architectural characteristics (highly parallel configuration),

(b) Artificial intelligence support of decision making, and

(c) New paradigms (non-contact testing scenario, controlled dialogue

scenarios, flexible source use, and the possibility of interaction through an artificial intelligence supported machine-human interface).

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The idea of modeling biometric data for decision making support

enhancement at checkpoints is explored, in particular, at theBiometric Technologies Laboratory at the University of

Calgary(http://enel.btlab.ucalgary.ca).

Simulators of biometric data are emerging technologies foreducational and training purposes (immigration control,

bankingservice, police, justice, etc.). They emphasize decision-

making skillsin non-standard and extreme situations.

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