  In this paper, a multi-resolution feature extraction algorithm for face
recognition is proposed based on two-dimensional discrete wavelet transform
(2D-DWT), which efficiently exploits the local spatial variations in a face
image. For the purpose of feature extraction, instead of considering the entire
face image, an entropy-based local band selection criterion is developed, which
selects high-informative horizontal segments from the face image. In order to
capture the local spatial variations within these highinformative horizontal
bands precisely, the horizontal band is segmented into several small spatial
modules. Dominant wavelet coefficients corresponding to each local region
residing inside those horizontal bands are selected as features. In the
selection of the dominant coefficients, a threshold criterion is proposed,
which not only drastically reduces the feature dimension but also provides high
within-class compactness and high between-class separability. A principal
component analysis is performed to further reduce the dimensionality of the
feature space. Extensive experimentation is carried out upon standard face
databases and a very high degree of recognition accuracy is achieved by the
proposed method in comparison to those obtained by some of the existing
methods.
