Biometric Recognition System

1. Biometric Recognition System Eric Dabkowski Fadi Azhari Group #7 ECE 445 Fall 2004

2. What it is it? • Biometrics deals with identification of individuals • Based on their biological, physiological, or behavioral features • Often used in protection, security, identity verification, authentication, or forensics • Able to replace “traditional” keys, cards, passwords, PIN, etc. Biometric Modes • Iris Recognition • Face Recognition • Hand Physiognomy • Vein Networks • Voice Recognition • Lip Movement • Fingerprint Matching • Ear Shape • Retinal Scan • Signature (real-time or static) Recognition • Keystroke Rhythm • Gait Patterns • Odor

3. Face and Voice RecognitionWith Decision-making Capabilities Create a database of people Biometric Identification: “One-to-many” matching Scan face and Voice to determine a match in database If either is a match, output a logic indicating “YES” - If there is a match, send a signal to remote receiver to indicate this.

4. Desired Traits and Benefits of Recognition System Accuracy Environment Ergonomics/User friendliness Stability and uniqueness of feature to be measured Security Safety Speeds of enrollment and recognition Non-intrusiveness/Convenience Cost/size Operation limitations/robustitude

5. Basic Biometric System

6. Original Proposed Block Diagram

7. Segments of Indentification System: • Data collecting • Image capture, voice recording        • Signal Processing • Perform respective algorithms for face and voice recognition • Storage and Decision Sub-system • Depending on threshold values (found through testing), determine whether or not a match was found • Transmission of Results • Use of QPSK modem to send positive match results

8. Schematic of Engineering Design

9. Storage and Decision Sub-systems Enrollment Phase: Biometric samples from a user are used to produce, generate, or train a model or pattern from the user.             This is the “key” process, resultant pattern or model represents the “identity card” of each enrolled user. Testing Phase:             Biometric samples are identified or verified against enrolled users. Errors           Type I – False Rejection (more  convenience) Type II – False Acceptance (more  secure)

10. Voice Recognition • Goal was to design a proper voice recognition system that was able to determine whether or not a match was made with the database.

11. Design • We decided to utilize that 54x board to handle the voice recognition aspect. • Benefits • Data outputted will be able to be used on QPSK with no new hardware. • Libraries are available that shall simplify our work later. • Such as fft, log, cfft, ifft.

12. Cepstral Analysis

13. Benefits of Cepstral • Extremely high efficiency. • Very simple to implement with our given libraries. • Once voice sample is taken, cepstral coefficients are able to be taken and stored easily into the database, requiring less storage.

14. Schematic for Voice Verification

15. Biometric Face Recognition • Benefits to using Face Recognition • Non-invasive method of identification • Extremely convenient • Cooperation is not necessary • Efficient and Effective

16. Face Recognition Schematic

17. Spatial Frequency Domain Image Processing • An easy way to compare an input to a template • Use of correlation filters in frequency domain • Output of correlation process may be interpreted • If peaks are above a certain threshold, there is a match Ways to Implement Face Recognition • Principal Component Analysis • 2-D Fourier Transform and Frequency Domain Filtering • Model based recognition

18. Image is captured and made discrete in pixels Transform to Spatial Frequency Domain(Correlation Process) • Image is downsampled to eliminate noise Fourier Transform • Input is Filtered • Inverse Fourier Transform • Decision-making begins

19. Correlation Outputs If PSR is larger than a certain threshold, then the input was a match. If it was below the threshold, then it was determined to not be a match.

20. QPSK Modem • Implement a QPSK modulator/demodulator system • Transmission over an acoustic (speaker/microphone) channel with low intersymbol interference

21. Real World Applications • Today’s most widely-used upstream modulation technology • QPSK used in • Cable telephony • Satellite modems • Benefits of QPSK • Same energy used for each symbol sent (good for applications where energy use is a prime concern) • Cost effective

22. Complete QPSK System Modulator (Transmitter) Demodulator (Receiver) Speaker Keyboard Microphone LED

23. Intended Design • The QPSK modem would work by obtaining a value from the combination of the voice/face recognition and transmit the data. • The receiver would work by obtaining the samples through a microphone and use a decision matrix to determine whether or not the LED should be lit up or not.

24. TransmitterThe output of the voice/face recognition was to be either a 1111 or a 0000. This shall be used to reduce the probability of error as 4 bits are sent, but only one will be used, whichever value was received the most. cos(λcn) a[k] x[k] = 2a[k] - 1 x[n] sI[n] p[n] Input data stream Series to Parallel Conversion s[n] I/Q Mapping D/A p[n] y[n] sQ[n] b[k] y[k] = 2b[k] - 1 sin(λcn)

25. Symbol ConstellationDue to the fact that (1,1) and (0,0) are separated by a higher distance, using these two will minimize the probability of error. (0,1) (1,1) (0,0) (1,0)

26. Receiver Symbol Timing Recovery 2cos(λcn) (p’[n] = p[n] * c[n]) kNT LPF p’[NT-n] Symbol Quantization and Unmapping r[n] Output Data Stream Carrier Recovery A/D kNT LPF p’[NT-n] Three samples/period 2sin(λcn)

27. Symbol Synchronization Delay-locked Loop

28. Carrier Recovery • Carrier Recovery • Detects phase offset between received and generated sinusoids and eliminates difference by adjusting phase of the sinusoids generated by the numerically controlled oscillator (NCO) Phase-locked Loop

29. Modeling the Channel • Speaker Frequency Response • -3dB frequency response from 90 Hz to 20 kHz • Microphone Frequency Response • Roughly unit gain from 200 Hz to 14 kHz • Approximate Channel Model: • Constant gain bandpass channel from 200 Hz to 14kHz Choose carrier frequency of 7.1 kHz (Digital frequency of .3220π)

30. Tests • Voice Recognition • Face Recognition • QPSK

31. Voice Recognition Test • Two types of tests were done • Test 1: Person A says key word, Person B says key word, Person A repeats key word. • Efficiency Rating 97%. • The threshold was determined to be 2.5 (Under 2.5 = match)

32. Voice Recognition Test Cont. • Test 2: • We created a database of Eric and my voice. We asked 10 students to come up and test their voice. Eric and I proceeded to test our voice as well. • Efficiency Rating 96%. • Used the same threshold: 2.5

33. Face Recognition Test • Tested pictures between Eric and I taken from the same spot, with the same lighting, and the same zoom. • Efficiency – 96.7% • Threshold was determined to be 4.57 (Over 4.57 = match)

34. Testing Results of Face Recognition

35. Testing Results Continued

36. QPSK Modem Test • In order to test the QPSK transmitter, we created a MATLAB simulation and compared it with the outputs of the 54x board with different inputs. • Efficiency Rating: 100%

37. Challenges Faced • Learning new language • Overly ambitious • Mislead advice from others • Many error factors in face

38. Successes • Working Voice Recognition system with better than expected efficiency. • Working Face Recognition system with better than expected efficiency. • Working QPSK transmitter • Learning about many different aspects of DSP

39. Failures • Unable to get QPSK receiver working. • Unable to connect all units together and work as one whole unit. • Face recognition only functional under specific conditions.