1. PROJECT TOPIC: FACE-RECOGNITION BASED DIGITAL SIGNATURE FOR
EMAIL ENCRYPTION AND SIGNING.
2. PROJECT GUIDE: Mr. TANDIN WANGCHUK PRJOJECT MEMBERS: Ms.
CHIMI WANGMO (eit2010011) Ms. PEMA YANGDEN (eit2011019) Mr. TASHI
TSHEWANG (eit2011029) Mr. THUKTEN LOBZANG (eit2011030)
3. SCHEDULE LITERATURE REVIEW CONCLUSION CHALLENGES
ACHIEVEMENTS PROBLEM STATEMENT AIM SCOPE METHODOLOGY FUTURE SCOPE
OUTLINES REFERENCES
4. INTRODUCTION
5. Dechen Tandin Dorji Dorji the Hacker wants to breach their
secure communications. LET US MEET WITH DECHEN & TANDIN
6. Dechen doesn't know whether shes talking to Tandin or Dorji.
Dechen doesn't know whether her sensitive information is safe from
Dorji's prying eyes.
7. Dechens Dechens
8. Dechens Public Key Dechens Private Key Anyone can get
Dechen's Public Key, but Dechen keeps his Private Key to himself
DECHENS CO-WORKERS Karma Tashi Sonam
9. HNFmsEm6Un BejhhyCGKOK JUxhiygSBCEiC 0QYIh/Hn3xgi K
BcyLK1UcYiY lxx2lCFHDC/A Dechen, did you go for lunch? HNFmsEm6Un
BejhhyCGKOK JUxhiygSBCEiC 0QYIh/Hn3xgi K BcyLK1UcYiY lxx2lCFHDC/A
Dechen, did you go for lunch?
10. PASSWORD/PIN
11. OTHER BIOMETRIC SYSTEM
12. AIM To sign and encrypt the email using the passphrase
based on face recognition.
13. SCOPE To generate passphrase based on face recognition in
order to protect private key with use of OpenCV framework. To set
up email environment for Encryption & Signing.
14. Digital Signature is implemented by Public and Private key
algorithm and hash function (A.Menezes, 1996) . Any public key
crytography algorithm may be used in the digital signature function
according to various embodiments of the invention, but for the
purposes of illustrations, Literature Review RSA is described since
it is ideally suited to digital signature functions.
15. It includes the minimum distance classification in the
eigenspace (Turk and Pentlands, 1991; Belhumeur.1997), the
independent face space based on independent component analysis
(ICA), the discriminative subspace based on Linear Discriminate
Analysis (LDA), neural networks based classifiers (Fleming and
Cottrell, 1990) and probabilistic matching based on
intrapersonal/extra personal image difference (Teixeira and
Beveridge, 2003). In the literature, several classifiers are
proposed for face recognition.
16. A number of earlier face recognition algorithms are based
on feature-based methods that detect a set of geometrical features
on the face such as the eyes, eyebrows, nose, and mouth. Properties
and relations such as areas, distances, and angles between the
feature points are used as descriptors for face recognition. (B.S.
Manjunath, R. Chellappa, C. Von der Malsburg, 1992) Feature-based
methods
17. METHODOLOGY
18. Literature Review Requirement Gathering & specification
Face Recognition system Setting up of email client Face Detection
Feature Extraction Face Recognition Identification &
Verification Passphrase generation
19. a. SETTING UP OF EMAIL CLIENT ENCRYPTION1 ENCRYPTION REPLY
DECRYPTION DECRYPTION REPLY 1 1 1
20. b. FACE-RECOGNITION
21. PROJECT SCHEDULE
22. ACHIEVEMENTS: i. FACE DETECTION & FEATURE
EXTRACTION:
23. ii. TRAIN RECOGNIZER:
24. iii. IDENTIFICATION OR VERIFICATION:
25. GRAY SCALE IMAGE STORAGE:
26. iv. CODE GENERATION Based on Simple principle for Random
key generation, Probability of facial Similarity >= 95%, same
code Probability of facial Similarity