Deep learning has impacted the research and application of face recognition to some extent; however, it is unsuitable for small embedded devices owing to its large computational cost and time consumption. Herein, a facial feature extraction method for integrating gradient features in a lightweight convolutional neural network (SqueezeNet) was proposed to ensure the application of the network model to embedded devices with relatively small memory and facial features that are more robust to different lightings. Experimental results showed that the lightweight convolutional neural network integrating the first-step gradient feature extracted by dividing the image into a block of 8 × 8 can achieve a recognition rate of up to 97.28% in LFW dataset, which is 4.36% higher than that of the conventional lightweight convolutional neural network.