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In the embedded system environment, both large amount of face image data and the slow recognition process speed are the main problem facing face recognition of end devices. This paper proposes a face recognition algorithm based on dual-channel images and adopts a cropped VGG-like model referred as VGG-cut model for predicting. The training set uses the same single-layer images of the same person combined into dual channels as a positive example, and single-layer images of different people are combined into dual channels as a negative example. After model training, the trained face verification model is used as the basis of face recognition, and the final recognition result can be obtained through loop matching and similarity ranking. The experimental results on a RISC-V embedded FPGA platform show that compared with the ResNet model called in Dlib, the VGG-like model and MobileFaceNet trained by Keras, our algorithm is increased by 240×, 88×, and 19× in recognition speed, respectively without significant accuracy reduction.