Face Detection and Modeling for Recognition - Biometrics Research ...

mouth is performed within the face mask. The eye and mouth candidates are located
by using (i) a pyramid decomposition of the eye/mouth maps and (ii) an iterative
thresholding and binary morphological closing on the enhanced eye and mouth maps.
The number of pyramid levels, L, is computed from the size of the face candidate, as
defined in Eqs. (3.10) and (3.11).
L = Max { ⌈log 2 (2σ)⌉, ⌊log 2 (Min(W, H)/F c )⌋ } ; (3.10)
σ = ⌊ √ W · H / (2 · F e )⌋ , (3.11)
where W and H represent the width and height of the face candidate; F c × F c is the
minimum expected size of a face candidate; σ is a spread factor selected to prevent
the algorithm from removing small eyes and mouths in the morphological operations;
and F e is the maximal ratio of an average face size to the average eye size. In our
implementation, F c is 7 pixels, and F e is 12 pixels.
The coarse locations of eye and mouth candidates obtained from the pyramid decomposition
are refined by checking the existence of eyes/mouth blobs which are obtained
after iteratively thresholding and (morphologically) closing the eye and mouth
maps. The iterative thresholding starts with an initial threshold value, reduces the
threshold step by step, and stops when either the threshold falls below a stopping
value or when the number of feature candidates reaches pre-determined upper bounds,
N eye for the eyes and N mth for the mouth. The threshold values are automatically
computed as follows.
T h = α n
∑
(x,y)∈FG
Map(x, y) + (1 − α) · Max Map(x, y), (3.12)
(x,y)∈FG
75