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In our social life face is the primitive center for consideration, which plays a vital task for identification and an emotion. Live Face Recognition has attained enormously consideration in security systems, due to its non intrusiveness, accurate and fast results etc. Real time human Face recognition executes in two levels, such as Face detection and Face recognition. For face detection we are using viola-jones algorithm because of its high precision and high real time permit rate which is deployed in opencv by python language. In Face recognition two categories are considered: training phase and evaluation phase. In training phase, the algorithm is trained with the samples of the image to be learned and In estimation phase, the test image is compared among all the trained samples in the dataset. The facial features is extracted with the detected faces from live stream by Local Binary Patterns Histogram (LBPH). By using the LBPH, face recognition is finished using Euclidean distance classifier. The Experimental results getting after implementations and testing is of accuracy 85%-95%.

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