Face Recognition Based on HOG Multi-feature Fusion and Random Forest

A novel approach to face recognition,which is based on HOG multi-feature fusion and Random Forest,was proposed to solve the problems of low face recognition rate in complex environments.This approach introduces the HOG descriptor(Histograms of Oriented Gradients)to extract information of the facial feature.Firstly,the face image grid is set to extract the holistic HOG features of the entire face,and the face image is divided into homogeneous subblocks,and local HOG features are extracted in the sub-blocks which contain key components of the face.After that,the dimensions of holistic and local HOG features are reduced using 2D Principal Component Analysis(2D PCA)and Linear Discriminant Analysis(LDA)and the final classification features are formed by the feature level's fusion.Finally,the random forest classifier is employed to classify the final features.Experimental results on FERET CAS-PEAL-R1 and real scene database demonstrate that the proposed approach not only significantly raises the recognition rate and reduces the computing time but also has certain robustness to the influence of light.