Face Detection EE368 Final Project Group 14 Ping Hsin Lee Vivek Srinivasan Arvind Sundararajan

Overview Introduction Methods used to detect faces Color segmentation Morphological Processing Template Matching And Clustering Results Techniques considered but not used

Color Segmentation Use color information in the YCbCr domain YCbCr Color space effectively decorrelates the intensity and color information Each channel information is represented in discrete levels.

MAP Rule Implement MAP decoder to determine skin from non-skin pixels D(I(x,y)) = 1 if P(I(x,y) | S)P(S) > T* P(I(x,y) | NS)P(NS) = 0 other wise Minimizes misclassification error

Result of Color Segmentation

Morphological Processing Reject blobs of small sizes, perform closing, remove holes

Non-face Object Removal Use information about shape and location of objects in conjunction to reject non-face objects while minimizing rejection of faces Objects characterized by max/min as a measure of length. (Independent of size, translation, and rotation of objects) Example of non-face object removed by CCA

Non-face Object Removal Before and after rejection

Template Matching Performed in the luminance domain using the FFT First attempt: use the average of all face regions Features did not seem to align properly, hence this template was rejected Rejected Template

Final Templates Used Resample each face region to the same size before averaging. Include mirror images of each face region to produce a symmetric template (a). In addition, a non-symmetric partial template (b) is used to capture information about smaller and partially obscured faces in the image One template tests for symmetry, while the other tests for non-uniform illumination, and captures smaller faces as well.

Clustering of Correlation Peaks The autocorrelation results for each template were first thresholded and then combined. Used heuristic techniques based on shape of the skin regions to group peaks. Any 2 peaks meeting a maximum distance criterion and connected by a line passing through only skin regions were grouped together as a face.

Results of Grouping Correlation Peaks Before and after peak grouping

Results Applied to the Original Image Image corresponding to the grouped peaks

Final Results Image 1 2 3 4 5 6 7 Score 20 22 24 21 Total 25

Techniques Considered but not Used Fisher’s linear discriminant (FLD) Poor performance in rejection of false positives because detected non-face and face regions are not linearly separable Eigenfaces Produced results similar to template matching but at an increased computational cost

Techniques Considered but not Used Support Vector Machines (SVM) Generated 470 face regions and 500 non-face regions each of size 49x55 pixels as training database Employed a Gaussian radial basis function (RBF) as kernel

Samples of database images Faces Non-faces

Results of SVM Produced decision regions that are too tightly bound to the training face samples and were not able to classify the faces in the other training pictures Including the SVM in the program would only slow down our runtime and would not produce noticeable improvements

Conclusion Color segmentation in the YCbCr domain and morphological processing produced good estimates of face regions Implemented multi-resolution template matching and peak clustering to further distinguish different face regions from each other and from non-face regions Could have done more to reject false positives (MRC/neural networks to reject hand regions)