1. 1. PROJECT TOPIC: FACE-RECOGNITION BASED DIGITAL SIGNATURE FOR EMAIL ENCRYPTION AND SIGNING.
2. 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. 3. SCHEDULE LITERATURE REVIEW CONCLUSION CHALLENGES ACHIEVEMENTS PROBLEM STATEMENT AIM SCOPE METHODOLOGY FUTURE SCOPE OUTLINES REFERENCES
4. 4. INTRODUCTION
5. 5. Dechen Tandin Dorji Dorji the Hacker wants to breach their secure communications. LET US MEET WITH DECHEN & TANDIN
6. 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. 7. Dechens Dechens
8. 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. 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. 10. PASSWORD/PIN
11. 11. OTHER BIOMETRIC SYSTEM
12. 12. AIM To sign and encrypt the email using the passphrase based on face recognition.
13. 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. 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. 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. 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. 17. METHODOLOGY
18. 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. 19. a. SETTING UP OF EMAIL CLIENT ENCRYPTION1 ENCRYPTION REPLY DECRYPTION DECRYPTION REPLY 1 1 1
20. 20. b. FACE-RECOGNITION
21. 21. PROJECT SCHEDULE
22. 22. ACHIEVEMENTS: i. FACE DETECTION & FEATURE EXTRACTION:
23. 23. ii. TRAIN RECOGNIZER:
24. 24. iii. IDENTIFICATION OR VERIFICATION:
25. 25. GRAY SCALE IMAGE STORAGE:
26. 26. iv. CODE GENERATION Based on Simple principle for Random key generation, Probability of facial Similarity >= 95%, same code Probability of facial Similarity