Deep Convolutional Neural Networks (DCNNs) are widely used to extract high-dimensional features in various image recognition tasks and have shown significant performance in face recognition. However, accurate face recognition in real-time remains a challenge, mainly due to the high computation cost associated with the use of DCNNs and the need to balance precision requirements with time and resource restrictions. Besides, the supervised training process of DCNNs requires a large number of labeled samples. Aiming at solving the problem of data insufficiency, this study proposes a Deep Convolutional Generative Adversarial Net (DCGANs) based solution to increase the face dataset by generating synthetic images. Our proposed face recognition approach is based on FaceNet model. First, we perform face detection using MTCNN. After, a 128-D face embedding is extracted to quantify each face and a Support Vector Machine (SVM) is applied on top of the embeddings to recognize faces. In the experiment part, both LFW Database and Chockepoint video Database showed that the proposed approach with DCGANs data augmentation has improved the face recognition performance.