Among the most widely cited methods for face recognition based on feature extraction are Eigenfaces (Turk et.al,1991) based on Principal Component Analysis (PCA), Fisherfaces (Belhumeur et.al,1997) based on Linear Discriminant Analysis (LDA), and methods based on Independent Component Analysis. A novel, pose-invariant face recognition system based on a deformable, generic 3D face model, that is a composite of:  an edge model,a color region model and a wireframe model for jointly describing the shape and important features of the face.

The first two submodels are used for image analysis and the third mainly for face synthesis. In order to match the model to face images in arbitrary poses, the 3D model can be projected onto different 2D viewplanes based on rotation, translation and scale parameters, thereby generating multiple face-image templates(Lee et.al,2002).Machine recognition of human face from still and video images has become an active research area in the field of image processing, pattern recognition, neural networks and computer vision. This interest is motivated by wide applications ranging from static matching of controlled format photographs such as passports, credit cards, driving licenses, and mug shots to real-time matching of surveillance video images presenting different constraints in terms of processing requirements (Chellappa et.al,1995).A general and efficient design approach using a radial basis function (RBF) neural classifier to cope with small training sets of high dimension, which is a problem frequently encountered in face recognition, is presented in the above research conducted and hybrid learning algorithm is implemented for the study.

The principal component analysis is one of the most popular multivariate statistical techniques(H.Abdi et.al,2010) has been widely used in the areas of pattern recognition and signal processing. There are numerous PCA based methods used for face recognition from one dimensional PCA to two directional two dimensional PCA .This research method promotes the accuracy compared to  one dimensional PCA,two dimensional PCA and two directional two dimensional PCA. One of the important existing challenges is developing an accurate real-time face recognition system which does not require to have high computational cost. Previous research works conduct a real-time face recognition system by using block processing of local binary patterns of the face images captured by NAO humanoid is proposed.

The proposed method has been adopted on NAO humanoid and tested under real-world conditions.(Bolotnikova et.al,2017).

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