Face recognition technology is one of the everyday tasks in our daily life. But, recognising the correct face with high accuracy from large databases is a challenging task. To overcome this challenge, feature fusion of local binary pattern (LBP) with Gabor-Kernel Fisher analysis (Gabor-KFA) has proposed for face recognition. In this method, by using Gabor filter, extract Gabor features from a face image, on the other hand, extract features from LBP coded face image, then combined these extracted features generate high dimensional feature space. With this high dimensionality features, the complexity of training time and identification time may increase. To avoid this complexity, the Kernel Fisher analysis algorithm was adopted to reduce the feature vector size. Experiments were conducted separately on Gabor features and also on fused features. To test the performance of the proposed approach, the experiments were performed on the IIT Delhi database, ORL database, and FR database.