1. Content Based Coding of Face Images
Introduction
Present day applications demand efficient image compression techniques.
Image Compression techniques based on removing statistical & subjective redundancy.
The project concentrates on the class of images whose Region of Interest is HUMAN FACE.
Applications: Face recognition, identity & credit card verification, video conferencing & model based coding of images.

2. Stage I : Image segmentation based on Chrominance
Done in wavelet domain.
The skin tones are distributed over a very small area in the chrominance plane (such as (Hue, Saturation or U,V)).
U and V chrominance components used in compression standards, such as, JPEG and MPEG. The human skin­tone is such that in chrominance domain 0.3 < Cb < 0.5 and 0.5 < C r < 0.7.
The above classifier is applied to (U, V) values corresponding to LL sub images of chrominance to check for candidate face pixels. Simple thresholding based on this gives a binary mask.
Each value in the mask image indicates the classification results of the corresponding block of size 16 x 16 in the original image.

3. Stage II: Detecting Face Regions
Chrominance information alone not enough to detect face regions.
To remove false alarms, apply shape constraints of human faces on the binary mask images generated by Stage I.
Shape of human face is consistent & unique. This can be exploited using binary template matching.
Face Template 1
Background
Face Region N 1 M 1

4. Stage II
Rectangles with certain aspect ratios is used as the boundary of the face regions. The range of aspect ratio of these bounding rectangles is between
[1, 1.4].
Rectangles also bounded by size. Lower limit of face size: 2x2.
The binary template consists of two parts: the face region, which is shaded rectangle of size M × N , and the background, which is the area between the inner and outer rectangles.
Match
No Match

5. Stage II Matching Criterion:
The two frame template is slided over the image.
The number of ones covered in the shaded region and the number of ones covered in the background region are counted.
For a match, the number of pixels in the shaded region should be large, and the number of pixels in the background should be small.

6. Stage II Morphological Operations: Alternative to template matching.
Binary mask first dilated with structuring element of size 17x17 to fill up holes.
Erosion with structuring element of size 17x17 restores the actual size of the face after the dilation.
Final erosion with a structuring element of size 4x3 to remove false alarms.
Final Output: mask corresponding to face position.

7. EXTRA IMAGE COMPRESSION OF APPROX. 40% OVER JPEG
Face Detection & JPEG
Discriminative Quantization.
Scaled Quantized Matrix for the Region of Interest, ie, FACE
Background not given importance.
Results: Quality of ROI (human face) remains same.
Blurred unimportant background details
Higher Compression Ratio
EXTRA IMAGE COMPRESSION OF APPROX. 40% OVER JPEG

8. Scope
Optimization of Face Detection Algorithm by taking into account energy distribution of wavelet coefficients.
Grouping the wavelet coefficients.
Calculating the total energy of all wavelet blocks.
Thresholding & verification of the results of Stage II of algorithm.
Experimenting the face detection algorithm with video codec.
Using neural network (trained data set) for Stage I of algorithm.

9. JPEG with Face Detection
Original Image
Extra Compression Ratio over JPEG: %

10. Original Image
Quantization scale: 4
Extra Compression over JPEG: %
Quantization scale: 2
Extra Compression over JPEG: