基于改进MobileFaceNet的人脸识别方法北大核心

针对目前大多数人脸识别算法参数多、计算量大,难以部署到移动端和嵌入式设备中的问题,提出了一种基于改进MobileFaceNet的人脸识别方法。通过对MobileFaceNet模型结构的调整,将bottleneck模块优化为sandglass模块,改良深度卷积和逐点卷积的相对位置,适当增大sandglass模块的输出通道数,从而减少特征压缩时的信息丢失,增强人脸空间特征的提取。实验结果表明:改进后的方法在LFW测试数据集上准确率达99.15%,模型大小和计算量分别仅为原算法的61%和45%,验证了所提方法的有效性。

Face Recognition Method Based on Improved MobileFaceNet

Most current face recognition algorithms are difficult to deploy to mobile terminals and embedded devices because of their many parameters and large amount of calculation, thus a face recognition method based on improved MobileFaceNet is proposed. By adjusting the structure of the MobileFaceNet model, the bottleneck module is optimized to the sandglass module, the relative position of depthwise convolution and pointwise convolution are improved, and the number of output channels of the sandglass module is appropriately increased, thereby reducing information loss during feature compression and enhancing extraction of the facial spatial features. The experimental results show that the improved method achieves an accuracy of 99.15% on the LFW test data set, and the model size and calculation amount are only 61% and 45% of that of the original algorithm, verifying the effectiveness of the proposed method.