Abstract
A multiresolution 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 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 bands precisely, the horizontal band is segmented into several small spatial modules. The effect of modularization in terms of the entropy content of the face images has been investigated. Dominant wavelet coefficients corresponding to each module residing inside those bands are selected as features. A histogram-based threshold criterion is proposed to select dominant coefficients, which drastically reduces the feature dimension and provides high within-class compactness and high between-class separability. The effect of using different mother wavelets for the purpose of feature extraction has been also investigated. PCA 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.
Highlights
Automatic face recognition has widespread applications in security, authentication, surveillance, and criminal identification
In order to obtain high within-class compactness as well as high between-class separability, we have considered wavelet coefficients corresponding to some smaller spatial modules residing within a horizontal band, which are capable of extracting variation in image geometry locally
The proposed wavelet-based dominant feature extraction algorithm provides an excellent space-frequency localization, which is clearly reflected in the high within-class compactness and high between-class separability of the extracted features
Summary
Automatic face recognition has widespread applications in security, authentication, surveillance, and criminal identification. The main advantage of biometric features is that these are not prone to theft and loss and do not rely on the memory of their users. Face is getting more popularity because of its nonintrusiveness and high degree of security. Unlike iris or finger-print recognition, face recognition do not require high precision equipments and user agreement, when doing image acquisition, which make face recognition even more popular for video surveillance. Face recognition is a complicated visual task even for humans. Some aspects of the image, such as variations in illumination, pose, position, scale, environment, accessories, and age differences, make the recognition task more complicated. Despite many relatively successful attempts to implement face recognition systems, a single approach, which is capable of addressing the hurdles, is yet to be developed
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