Census Transform and Faces

Another transform to the bag of so many feature extraction methods, the Census Transform (CT) and Modified CT (MCT) seems to be quite an interesting way of representing images in a very local neighbourhood. The transform provides high resilience against global illumination changes and thus is quite useful for face detection across wide illumination problems. Combined with the standard AdaBoost it proves to be an effective face detector scheme. Additionally it seems to be also used in applications in image retrieval research too. Histograms of the transform too have been used for further analysis.

The concept of CT is extremely simple. Used on grayscale images, consider any pixel and its 8 neighbours. Just assign boolean values 0/1 to the pixels who have a value lower/higher than the center respectively. Scan them in row order and this generates an 8-bit stream for each pixel and is the new transform value at that pixel. The central pixel is ignored.

On the other hand, the MCT makes a small change in this by saying compare with the mean of the 3×3 block rather than the center value. It can now also use the central pixel for comparison. A similar operation provides 9 bits which are the transform value for that pixel. A different block size like 5×5 can be used too for both, however 3×3 is usually the more favoured one.

An example figure from the paper – Face Detection with Modified Census Transform is shown here and clearly shows that the vast change in global illumination or gradient doesn’t affect the local pattern much.

Face Detection with MCT

Face Detection with MCT


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