Research On Low Resolution And Covered Face Detection

With the rapid development of modern artificial intelligence information technology,new technologies such as face recognition have gradually emerged,and face detection technology has become increasingly important.The early various face image detection technologies were mainly used for researching various face detection images with strong binding and conditions,such as those without any background and no occlusion,so these faces are easily Locate and find.Due to the continuous development of artificial intelligence.Face detection and recognition technology has become the focus of research.However,due to some scenes,such as surveillance scenes shot by shooting cameras.The face target is far away from the camera device or due to light,occlusion and other reasons.A series of problems faced by this also make the current face image detection technology begin to be paid attention by many researchers as an independent research topic.There are many difficulties in face detection technology.There are three main aspects.Faces can be blocked at different angles.The angle of imaging is different,which leads to the side of the image or the rotation of the face.Human faces have different skin tones.The environmental conditions for imaging are different.Such as lighting conditions,the presence or absence of shadows,etc.Therefore,this paper has carried out the following work on the low-resolution and occlusion face research:First,the framework of the MTCNN network is described,and its three-stage cascaded modules are introduced.A network structure based on MTCNN is proposed.Pre-process the pictures input to the P-Net network.Then the P-Net,RNet and O-Net networks were improved.And this method has achieved good results on WIDER FACE and FDDB data sets.Next,use semantic segmentation network to assist face mining and detection.The semantic segmentation network that can effectively improve the detection rate of difficult faces is studied.The semantic segmentation network is used for target detection.Here,the semantic segmentation network is used for face detection.When the face is blocked in varying degrees.Can assist face detection.Mining the information around the face.Using Fast-SCNN network,it uses a learning downsampling module,a standard global feature extractor processing module,a global feature data fusion processing module,and a standard classifier.All modules are constructed with deep separable convolutions.It can effectively improve the detection rate of difficult faces.Finally,a five-point positioning method for the key points of the face connected with the SSD network by the residual network is proposed.The premodule of the network uses a residual module.Its network performance far exceeds the traditional network model.Connect an SSD network behind.SSD networks are also detected using CNN.It uses a variety of data enhancement methods,including horizontal flip,crop,zoom in,zoom out,etc.The SSD extracts the face frame.Then five-point positioning of key points on the face.Get a precise face frame.And you can see that the effect on the COFW dataset is better.Therefore,it is superior to the detection of blocked faces.To sum up,this article is to study the face with low resolution and occlusion.These methods have more or less limitations.But the method in this article can deal with most faces.And can be detected and located at the same time.