1. USER VERIFICATION SYSTEM Scope Develop a User Verification System based on the application of one or more pattern recognition techniques. To begin with Face Recognition technique is used. The scope includes: Collection of Data Development of Database Development of User Interfaces Feature Extraction Integration of the system Testing and Delivery

2. Risk Analysis No Project Risks No Business Risks Few Technical Risks with reference to Data base and User Interface are expected. However, we have budgeted for the same and would avoid the occurrence of such Technical Risks.

3. Use Cases Essential Use Case 1: Known user gaining access Known user approaches the verification interface terminal Initiates interaction with the system and follows the procedure Analysis is carried out on the image and user’s data User is confirmed as registered user and admittance is allowed Essential Use Case 2: Collection of new user data Administrators of the system interact with a separate interface to feed the new user data including the test photographs Analysis and Feature extraction is done Then the system is trained using those test photographs The system’s acceptance of the new user is tested by initiating the user recognition sequence from user case 1.

4. Use Case Diagram

5. Use Case - Steps I Name: Enter Facility Description: A user enters the facility Precondition: The user seeks entrance to the facility Postcondition: The user enters the facility Primary & alternate courses of action: As detailed in the report II Name: Add User Description: A user is added to the user verification system Precondition: A valid user seeks access to the facility Postcondition: The user is added to the user verification system Primary & alternate courses of action: As detailed in the report

6. Use Case Scenario Scenario: A returning user is unrecognisable Description: The User Verification System is in use at a highly sensitive research lab of a large university. An esteemed professor returns from six months of field work and needs to enter the facility to begin work there. The professor’s appearance has significantly altered during his absence (he has grown thinner and is deeply tanned). Though the professor is a registered user, the User Verification System will fail to recognize him. Steps: As detailed in the report.

7. Data Flow Diagram Level 1

8. Results Training Set I 25 photographs were used to train the system 25 persons were tested 12 correct results 2 partially correct results Success rate 56% Training Set II 225 photographs were used to train the system 25 persons were tested 14 correct results 2 partially correct results Success rate 64% The use of more number of photographs of the same person in different angles increases the discriminatory power of the system thereby yielding a higher Success Rate.

9. Recommendations Compute Mean face for each person Use more number of photographs in different angles to train the system Use more principal components Use good quality pictures in training the system.