一种改进的基于SSD模型的多尺度人脸检测算法

An Improved Multi-scale Face Detection Algorithm Based on SSD model

目前基于单一卷积神经网络(SSD)的人脸检测模型在解决不同尺度的人脸检测问题时,存在小尺度人脸检测准确率不高的问题。为此,本文提出一种改进的基于SSD模型的多尺度人脸检测方法。该方法采用特征密集连接策略,加强基础网络中不同卷积层之间的信息流动性,从而提升基础网络的特征描述能力;并通过在浅层特征中引入上下文信息,提高小尺度人脸的检测准确性。在WIDER FACE数据集上进行实验,结果表明,本文方法可较好的检测出多尺度人脸。

At present,the face detection model based on single convolutional neural network(SSD)has the problem of low accuracy of small-scale face detection when solving the problem of face detection at different scales.Therefore,we propose an improved multi-scale face detection method based on SSD model.The method adopts the feature-dense connection strategy to improve the network structure of the basic network in the SSD model,strengthening the information mobility between different convolutional layers and improving the feature description ability of the basic network.By introducing context information into shallow features,the accuracy of small scale face detection is improved.Experiments on WIDER FACE data set have shown that this method can be used to detect Multi-scale face well.