2.3.6. WAV

WAV is a filtering technique that processes an image with different wavelets. With WAV, statistical indicators are extracted (e.g., the mean and variance) and used for discarding candidate images with no faces. Rejection is based on five criteria.

The first criterion applies phase congruency [64] to the depth map of the largest cluster, and the average value is used to discriminate between face/non-face. The segmentation process divides the image into multiple clusters, and only the largest cluster (that is, the one that is most likely to contain the face) is considered. Phase congruency has higher values when there are edges. WAV keeps only those candidates with an acceptable value, i.e., those with a number of edges that is neither too high nor too low, and deletes all others since they most likely contain no faces.

WAV is used here in two ways, but in both cases, Haar-like waves are selected since they often give the best results, as demonstrated in [65]. The first method (second criterion) works on the same principle as the phase congruency test: the Haar wave is applied to each image, and the average value is calculated for each one. However, the second test (third criterion) follows the approach in [50], where edge maps are first extracted and then fitted to an ellipse (the typical shape of a face). If an ellipse is found, then the image is rotated by an angle given by the intersection between the origin and the major axis of the ellipse, and the filter is applied to the rotated image. If no elliptical shape is found, the filter is applied to the original unrotated image. To conclude, the WAV filter produces higher values when it encounters specific features, especially abrupt changes that are typically not present in many non-faces.

Two remaining tests (fourth and fifth criteria) are based on Gabor's logarithmic wavelet filter for finding the symmetry of the shape of the largest cluster. We calculate the phase symmetry of points in an image. This is a contrast invariant measure of symmetry [64]. High values indicate the presence of symmetry, which can mean the presence of a symmetrical shape, such as an ellipse, and therefore that have a good probability of containing a face. The first test discriminates based on the average of the scores, while the latter uses variance instead of the mean.
