随着基于深度学习的人脸识别算法的发展和应用，人脸识别算法已经可以运用在计算资源充足的设备上并取得很高的精度和较快的速度，但是在计算资源受限设备上的应用有诸多困难。基于深度学习的人脸识别模型有着更好的识别精度，但是大多数基于深度学习的模型均需要大量的计算资源来支持运行。本文针对这一问题，设计出一个占用少量计算资源的基于深度学习算法的轻量级网络模型Lite-Inception-ResNet，该模型基于具有良好性能的Inception-ResNet模型，在保持原模型良好性能的基础上对卷积核和网络架构进行了优化和重新设计，并选用了性能更好的激活函数。在VGGFace2和LFW上的实验表明，新模型可以在LFW数据集上仅降低0.1%正确率的情况下减少88.2%的参数量和76.5%的计算量，使该模型可以较好地应用于计算资源较少的设备上。 With the development and application of deep learning-based approaches, face recognition algorithms have already been used on devices with sufficient computing resources and achieved high accuracy and fast speed. Face recognition models based on deep learning have better recognition accuracy, but requiring a large amount of computing resources. Aiming to this problem, this paper designs a model called Lite-Inception-ResNet, which is a lightweight network model based on deep learning algorithms and requires much fewer computing resources. The proposed model is based on the Inception-ResNet model and improved in network architecture and activation functions. Experiments on VGGFace2 and LFW show that the Lite-Inception-ResNet model can reduce the amount of parameters by 88.2% and the amount of calculation by 76.5% with only a 0.1% accuracy reduction, making the model more suitable for devices with less computing resources.
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机译：随着基于深度学习的人脸识别算法的发展和应用，人脸识别算法已经可以运用在计算资源充足的设备上并取得很高的精度和较快的速度，但是在计算资源受限设备上的应用有诸多困难。基于深度学习的人脸识别模型有着更好的识别精度，但是大多数基于深度学习的模型均需要大量的计算资源来支持运行。本文针对这一问题，设计出一个占用少量计算资源的基于深度学习算法的轻量级网络模型Lite-Inception-ResNet，该模型基于具有良好性能的Inception-ResNet模型，在保持原模型良好性能的基础上对卷积核和网络架构进行了优化和重新设计，并选用了性能更好的激活函数。在VGGFace2和LFW上的实验表明，新模型可以在LFW数据集上仅降低0.1%正确率的情况下减少88.2%的参数量和76.5%的计算量，使该模型可以较好地应用于计算资源较少的设备上。 With the development and application of deep learning-based approaches, face recognition algorithms have already been used on devices with sufficient computing resources and achieved high accuracy and fast speed. Face recognition models based on deep learning have better recognition accuracy, but requiring a large amount of computing resources. Aiming to this problem, this paper designs a model called Lite-Inception-ResNet, which is a lightweight network model based on deep learning algorithms and requires much fewer computing resources. The proposed model is based on the Inception-ResNet model and improved in network architecture and activation functions. Experiments on VGGFace2 and LFW show that the Lite-Inception-ResNet model can reduce the amount of parameters by 88.2% and the amount of calculation by 76.5% with only a 0.1% accuracy reduction, making the model more suitable for devices with less computing resources.
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