The Convolutional Neural Network (CNN) based on deep learning is introduced to propose two deep face detection algorithms and design an embedded face recognition system, in an effort to apply the deep learning algorithm to face detection and explore the embedded face recognition system. First, an optimized Multi-task Cascaded Convolutional Network (MTCNN) algorithm is proposed for the simulation transformation and crop preprocessing of the face image, denoted as OMTCNN. Second, a lightweight face recognition algorithm based on CNN is proposed to reduce the computational complexity of face recognition in the embedded system, denoted as LCNN. Finally, OMTCNN and LCNN are combined to construct the multi-core embedded face recognition system. Results demonstrate that OMTCNN shows good performance in determining face identity, and its training accuracy can reach 95.78%, significantly better than the unimproved MTCNN algorithm. On the Labeled Faces in the Wild (LFW) dataset, LCNN provides a correct rate of 98.13%, and the reduction in the complexity of the model itself increases the computational speed by as much as 6.4 times. The test results suggest that the designed face recognition system has good applicability on the embedded platform. The increase in the accuracy of the face recognition module and the introduction of the calculation acceleration module have significantly improved the face detection and recognition performance of the embedded system. The embedded face recognition system based on deep learning has practical application value.

介绍了基于深度学习的卷积神经网络（CNN），提出了两种深度人脸检测算法，并设计了一种嵌入式人脸识别系统，以期将深度学习算法应用于人脸检测并探索嵌入式人脸识别系统。首先，提出了一种优化的多任务级联卷积网络算法，用于人脸图像的仿真转换和作物预处理，称为OMTCNN。其次，提出了一种基于CNN的轻量级人脸识别算法，以降低嵌入式系统中人脸识别的计算复杂度，即LCNN。最后，将OMTCNN和LCNN相结合，构建了多核嵌入式人脸识别系统。结果表明，OMTCNN在确定人脸身份方面表现出良好的性能，训练精度达到95.78％，明显优于未经改进的MTCNN算法。在野外贴标（LFW）数据集上，LCNN的正确率为98.13％，而模型本身复杂性的降低使计算速度提高了6.4倍。测试结果表明，所设计的人脸识别系统在嵌入式平台上具有良好的适用性。人脸识别模块精度的提高和计算加速模块的引入极大地提高了嵌入式系统的人脸检测和识别性能。基于深度学习的嵌入式人脸识别系统具有实际应用价值。LCNN的正确率为98.13％，并且模型本身复杂度的降低使计算速度提高了6.4倍。测试结果表明，所设计的人脸识别系统在嵌入式平台上具有良好的适用性。人脸识别模块精度的提高和计算加速模块的引入极大地提高了嵌入式系统的人脸检测和识别性能。基于深度学习的嵌入式人脸识别系统具有实际应用价值。LCNN的正确率为98.13％，并且模型本身复杂度的降低使计算速度提高了6.4倍。测试结果表明，所设计的人脸识别系统在嵌入式平台上具有良好的适用性。人脸识别模块精度的提高和计算加速模块的引入极大地提高了嵌入式系统的人脸检测和识别性能。基于深度学习的嵌入式人脸识别系统具有实际应用价值。人脸识别模块精度的提高和计算加速模块的引入极大地提高了嵌入式系统的人脸检测和识别性能。基于深度学习的嵌入式人脸识别系统具有实际应用价值。人脸识别模块精度的提高和计算加速模块的引入极大地提高了嵌入式系统的人脸检测和识别性能。基于深度学习的嵌入式人脸识别系统具有实际应用价值。