Human face classification based on localized blur descriptors

In our proposed work localized patch based geometric blur point descriptors are accumulated to generate a global similarity matrix for every pair of focal and query image. A focal image is a randomly selected template image that represents each class and a query image symbolizes all other images that belong to the same class. The similarity matrix is dimensionally reduced using the proposed bidirectional 2-dimensional principal component analysis technique to generate distinctive feature sets. These feature sets are used for training and testing an extreme learning machine classifier. The proposed face recognition structure handles variations in head positions, lighting conditions, facial expressions and cluttered background by exclusively matching template and query images. Extensive experiments are performed using challenging face databases and significant improvements in recognition accuracy were achieved.