Selection and implementation of a face descriptor that is both discriminative and computationally efficient is crucial. Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) have been proven effective for face recognition. LBPs are fast to compute and are easy to extract the texture features. OC-LBP descriptors have been proposed to reduce the dimensionality of LBP while increasing the discrimination power. HOG features capture the edge features that are invariant to rotation and light. Owing to the fact that both texture and edge information is important for face representation, this article proposes a framework to combine OC-LBP and HOG. First, OC-LBP and HOG features are extracted, normalized and fused together. Next, classification is achieved using a histogram-based chi-square, square-chord and extended-canberra metrics and SVM with a normalized chi-square kernel. Experiments on three benchmark databases: ORL, Yale and FERET show that the proposed method is fast to compute and outperforms other similar state-of-the-art methods.

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This review paper is based on the versatile and numerous uses of bamboo grass plant. They are some of the fastest growing plants. Bamboos as construction material are associated with south and east Asia. Bamboos long life make it symbol of uprightness in china and symbol of friendship in India. It is used by rural people for food, housing and other domestic purposes. They are used in Chinese traditional medicine for treating various ailments and are rich source of nutrients. They can be further exploited for various other medicinal uses.

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