The Research And Implement Of Face Detection And Face Alignment In The Wild

Face detection and face alignment are well studied problems in computer vision and they are widely used in real life.Smart phones utilize face detection to take better pictures of people.Face alignment can help safe driving.Besides,face detection and face alignment play important role in face recognition and expression recognition.Many face detectors can easily detect near frontal faces without occlusion and under the circumstances,face alignment can accurately locate key points.But in real-word conditions,robust face detection and face alignment are very challenging due to the large variability of face caused by occlusion,pose and illumination.In this paper,we propose three methods and successfully improve the accuracy of face detection and alignment.First,this paper designs new network for face detection and face alignment by multi-task learning.Differing from other methods which jointly learn face classification,bounding box regression and facial landmark localization,our method adds another task to predict weather two pictures are of the same kind(if both two pictures are faces or non-faces,the label is one.Otherwise,the label is zero)and transforms the model form single input to two inputs.It also chooses different features according to assignments and improves the accuracy of face detection and alignment.This method outperforms the state-of-the-art methods on FDDB and WIDER FACE datasets.As for face alignment,it is also superior to other methods on LFPW dataset.Second,parts around face can help to detect small,vague and occluded faces.Therefore,this paper proposes to use semantic segmentation model to make the best of parts around face.In semantic segmentation,every pixel is marked.By strong-supervised learning,we can detect more small and occluded faces.Considering the slow speed of semantic segmentation models,this paper designs a new segmentation model for face detection.The method outperforms other methods on FDDB dataset which is trained on LIP dataset.Thirdly,in this paper,we achieve face alignment under large poses and partial occlusion by ignoring the points which cannot be seen under occlusion and extreme poses.The method first gets facial parts by the outputs of the multi-task learning proposed by this paper and then it further utilizes corner points and contours to get more facial parts.Finally we train regression models for every facial part to get key points.Because of only locating the truly existing points and utilizing facial parts to obtain key points,it leads to higher accuracy and reduces the adverse influence of the drift and shape of face detection results.The method outperforms the state-of-the-art methods on AFLW and COFW datasets and it is also comparable to other methods on LFPW dataset.In conclusion,this paper first utilizes multi-task learning and designs new networks to improve the accuracy of face detection and face alignment.Then,in order to detect faces and locate key points under occlusion and large poses,this paper takes advantages of semantic segmentation and facial parts separately.Experiments validate that the methods proposed by this paper can handle most of faces and accomplish face detection and alignment simultaneously.Therefore,it can be more widely used.