In the past decade, Improve the quality in face recognition system is a challenge. It is a challenging problem and widely studied in the different type of imag-es to provide the best quality of faces in real life. These problems come due to illumination and pose effect due to light in gradient features. The improvement and optimization of human face recognition and detection is an important problem in the real life that can be handles to optimize the error rate, accuracy, peak signal to noise ratio, mean square error, and structural similarity Index. Now-a-days, there several methods are proposed to recognition face in different problem to optimize above parameters. There occur many invariant changes in hu-man faces due to the illumination and pose variations. In this paper we proposed a novel method in face recogni-tion to improve the quality parameters using speed up robust feature and linear discriminant analysis for opti-mize result. SURF is used for feature matching. In this paper, we use linear discriminant analysis for the edge dimensions reduction to live faces from our data-sets. The proposed method shows the better result as compare to the previous result on the basis of comparative analysis because our method show the better quality and better results in live images of face.