基于深度学习的人脸识别算法研究

考虑到现实环境中的人脸图片在角度、光线、分辨率上的复杂程度，对Inception-ResNet-V1网络结构进行了改进，同时完成了数据集制作、超参数调节等相关工作，并在家庭服务机器人平台上进行了实验研究。实验结果表明，改进的网络结构在LFW测试集上准确率达到99.22%，高于原始网络结构的99.05%；在亚洲人脸数据集上准确率达到99.20%，高于原始网络结构的97.10%；在自建非匹配人脸数据集上误识别率为3.43%，低于原始网络结构的12.28%。可以看出，与原始网络结构相比，改进网络结构提升了人脸识别的准确率且降低了误识别率。

Face recognition algorithm based on deep learning

Considering the complexity of the angle, light and resolution of face images in real environment, this paper improved the network structure of Inception-ResNet-V1, completed the related work of dataset production and hyper-parameter adjustment, and carried out experimental research on the home service robot platform. The experimental results show that the improved network achieves 99.22% accuracy in LFW testset, which is higher than 99.05% of the original network structure. It reaches 99.20% accuracy on the Asian face dataset, which is 97.10% higher than the original network structure. The false recognition rate on self built mismatched face dataset is 3.43%, which is lower than 12.28% of the original network structure. It can be seen that compared with the original network structure, the improved network structure improves the accuracy of face recognition and reduces the false recognition rate.