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With the development of embedded technology and Internet of things technology, smart home has developed rapidly in recent years. At the same time, deep learning also has brought breakthroughs. The application of deep learning to smart home system can bring a good user experience and increase security. In this work, the application of the convolution neural network model, which is belong to deep learning method, in the field of human face recognition in natural scenes. Embedded devices collect and preprocess image and send it to the server. In the server, a improved lightened VGG network model has been designed, which is used to face recognition matches. This smart home system can reduce the computation of embedded devices, improve the accuracy of recognition. The test of this system meets the requirements of face recognition applications in the surveillance video. Keywords—smart home; embedded system; convolutional neural network; face recognition

Deep Learning Applied to Smart Home Face Recognition Access Control System