1. DESIGN AND DEVELOPMENT FOR FACE
RECOGNITION USING STEREO MATCHING
ALGORITHM
by
N.M.Harish Balaji
Sankara College of Science and Commerce

2. INTRODUCTION
• Face Recognition(FR) - images & videos.
• Face recognition compliments face detection .
• Face detection - finds faces in images and videos .
• Problems in FR - to handle pose variation .
• 2 predominant methods
1) Geometric approach
2) Photometric approach

3. SECTIONS IN FACE RECOGNITION
Face Recognition deals with 3 main sections, they are:
1. Images with 3 landmarks in face.
2. Illumination variation.
3. Pose variation.

4. BRIEF PROCESS
• FR handles pose & illumination variations.
• Gallery image is generated with 4 landmark points.
• Similarities are identified using matching cost.
• Works well for large pose variations.
• Dramatic changes is a challenging problem that an face
recognition system needs to face.

5. FEATURES
• Feature based system - detects - facial landmarks.
• Initially face images need to be aligned.
1. To generate landmark points - Eyes, Nose, Mouth.
2. Fourth landmark - stereo.
• Stereo - 3*3 filter - calculates the distance between
test image & training image.

6. FACE RECOGNITION METHOD
• Stereo Matching - supports good correspondence.
• Dynamic programming - 2D face images.
• Stereo algorithm - maximizes the cost function.

7. MATCHING PROCESS
• Matching - individual pixel intensities.
• Many matches .
• Right match - difficult .

8. STEREO MATCHING
• Stereo matching algorithm - individual pixel intensities.
• Objective - Matching 2 images.
• Matching - 2 scan lines l1 & l2.
• Cost of matching is given by
Matching cost = cost (l1,l2)

9. RECTIFICATION & MATCHING COST
• Rectification - calculates similarity between images.
• Recognition - matches images.

10. ILLUMINATION HANDLING
• Quite difficult - more changes than in real image.
• Chance of false detection.
• To overcome - Normalization.

11. RESULTS
• Performance is evaluated on real image.
• Image contains - flat regions, shadings & texture.

12. RECOGNITION RATE
To evaluate the performance of FR, recognition rate is
used
RR= (No. of correctly identified face)
(Total number of faces )

13. PERFORMANCE ANALYSIS
METHOD RECOGNITION RATE
LBP 82.50%
LTP 80.00%
NOVAL APPROACH 98.27%

14. BAR CHART REPRESENTATION
RECOGNITION RATE
100
80
60
RECOGNITION RATE
40
20
0
LBP LPT NOVEL APPROACH

15. CONCLUSION
• Simple general method - reduces illumination changes.
• Performance is good - accurate as well.
• ADVANTAGE : Automatic face recognition system.