1. EE368 Digital Image Processing Face Detection Project By Gaurav Srivastava Siddharth Joshi

2. Problem Definition To detect faces in a class group photograph. To differentiate female faces.

3. Challenges Varying lighting conditions. Various objects with pseudo-skin color. Occluded faces. Different scale size of faces. Faces in non-frontal position.

4. Approach Skin Color Segmentation Morphological Operations (Hole Filling, Erosion) Eigenspace Projection Density Estimation And Peak Detection Detecting Male/Female Faces Deciding Face/Non-face Input Image Output Image Block Diagram of Implementation

5. Skin Color Segmentation YCbCr Space Better Skin Color localization than HSV space. Invariant under various lighting conditions.

6. Result of Skin Color Segmentation

7. Morphological Operations Hole Filling. 1 st Level Erosion, Diamond structuring element. 2 nd & 3 rd Level Column Erosion. Selection of blocks, by size criterion.

8. Binary Image After Hole Filling

9. Different Levels of Erosion

10. Eigenspace Decomposition Training set of 53 facial images for KL Transform. First 20 eigenvectors used as Principal Components.

11. Gaussian F-space Density Estimation Estimation of the likelihood function for the image data – i.e. P(x| ). can be used to compute a local measure of the target saliency.

12. Detected Face Probability Density

13. RMS Detection Criterion Difference in reconstruction errors for Face/Non-face using eigenspace projections.

14. Gender Determination Projection calculations using multiple faces of a female. Calculation of RMSE of projections of a facial candidate with stored projections.

15. Original Image

16. Detected Faces: Male/Female

17. Conclusions Combination of deterministic algorithms like PCA, F-space density estimation and heuristics. Difficult to generalize the algorithm. Algorithm performs well on most frontal faces. Difficulty in detecting occluded faces.