Recently, face recognition applications achieved promising results by using Convolutional Neural Network (CNN). CNN has the capability to extract features automatically from images and does not need to extract hand-crafted features as traditional algorithms. Feature fusion aims to provide improvements of data validity for both traditional algorithms and deep learning algorithms. In this paper we propose a feature fusion approach for face recognition, the approach performs fusion at the feature level by applying two pre-trained CNNs AlexNet and ResNet-50. Firstly, extracting the feature from both pre-trained CNN AlexNet and ResNet-50 separately. Secondly, fuse the feature maps learned from AlexNet and ResNet-50. Finally, a Support Vector Machine (SVM) classier is used for the classification task. Experiments are conducted on the following datasets: FEI face, GTAV face, ORL, F_LFW, Georgia Tec Face, LFW, DB_Collection, demonstrate the effectiveness of the proposed approach. In addition, the fusion of the two CNN based models AlexNet and ResNet-50 lead to significant performance improvement. In particular, the fusion approach achieves accuracy in range (96.21%-100%) on all datasets.