The document summarizes examples of machine learning applications presented by a machine learning group. It discusses number plate recognition using SVM and neural networks for plate detection and recognition. It also discusses face recognition using Eigenfaces or Fisherfaces methods including face detection, preprocessing, training an algorithm, and recognition. Code examples are provided for both number plate recognition and face recognition.

1. Machine learning group
Practical examples
Scope
We have covered some basic theory in our machine learning
group but only a few of us have applied any of the tools.
The next few weeks we are presenting some practical examples.

2. 2
Content
Practical examples
1. Numberplate recognition
- Method
- Code
2. Face recognition
- Method
- Code
Material from the book - Mastering OpenCV with practical computer
vision examples, was used for this presentation.

3. 3
Examples
1. Numberplate recognition
Method
SVM and Neural Networks
1. Plate detection
- Segmentation
- Feature extraction
- Classification
- Results
2. Plate recognition
- Segmentation
- Feature extraction
- Classification
- Results

4. 4
Examples

5. 5
1. Plate detection
Examples
- Segmentation
- Feature extraction
- Classification
- Results

6. Sobel filter Threshold operation Close morphologic operation
Mask of one filled area Detected plates after the
6
Possible detected plates
marked in red (features images)
SVM classifier
Examples

7. 7
2. Plate recognition
Examples
- Segmentation (OCR segmentation)
- Feature extraction
- Classification
- Results

8. 8
Examples

9. 9
1. Numberplate recognition
Code
Examples

10. 10
Examples
2. Face recognition
Method
Eigenfaces or Fisherfaces
1. Face detection
2. Face preprocessing
3. Training a machine-learning algorithm from collected faces
4. Face recognition

11. 11
Examples
2000 – Slow and Unreliable face detection
2001 – Viola and Jones invented Haar-based cascade classifiers for object
Detection. Big improvement especialy for real time application.
2002 – Improved buy Lienhard and Maydt.
2006 – LBP features by Ahonen Hadrd (Faster than Haar based features)
LBP similar to Haar but uses histograms of pixel intensity comparisons.

12. 12
Examples
Viola and Jones
1. Grey scale image – intensity values from 0 to 255.
2. The feature value is calculated as the sum of the pixel intensities in the
light rectangle(s) minus the sum of the pixels in the dark rectangle(s)
3. These adjacent blocks are known as ’features’.The value of the feature
is then used in a filter to determine if that feature is present in the original
image
4. To make summing the intensity of the pixels in a given rectangle less
computationally expensive and improve the speed, the integral image gets
calculated (finding the area under a curve by adding together small
rectangular areas) at every point.
5. Learning. When there is no correlation between the feature identify-ing
a face and it not being one, and vice versa it would be rejected.

13. Type of cascade classifier XML filename – OpenCV v2.4
Face detector (default) haarcascade_frontalface_default.xml
Face detector (fast Haar) haarcascade_frontalface_alt2.xml
Face detector (fast LBP) lbpcascade_frontalface.xml
Profile (side-looking) face detector haarcascade_profileface.xml
Eye detector (separate for left and right) haarcascade_lefteye_2splits.xml
Mouth detector haarcascade_mcs_mouth.xml
Nose detector haarcascade_mcs_nose.xml
Whole person detector haarcascade_fullbody.xml
13
Examples

14. 14
Examples
Geometrical
transformation
and cropping
Separate
histogram
equalization
for left and
right sides
Smoothing
bilateral
filter
Elliptical mask

15. 15
Examples
Eigen Faces
Fisher Faces

16. 16
Examples
2. Face recognition
Code

17. Thank you
Name (email@csir.co.za)