Integrated Deep Model for Face Detection and Landmark Localization From “In The Wild” Images

IEEE Access

Ahmed Bouridane¹  Gary Storey¹  Richard Jiang¹ 

[1] Department of Computer and Information Sciences, Northumbria University, Newcastle Upon Tyne, U.K.;

关键词: Computer vision;    face detection;    machine learning;   

DOI  :  10.1109/ACCESS.2018.2882227

来源: DOAJ

【 摘 要 】

The tasks of face detection and landmark localisation are a key foundation for many facial analysis applications, while great advancements have been achieved in recent years there are still challenges to increase the precision of face detection. Within this paper, we present our novel method the Integrated Deep Model (IDM), fusing two state-of-the-art deep learning architectures, namely, Faster R-CNN and a stacked hourglass for improved face detection precision and accurate landmark localisation. Integration is achieved through the application of a novel optimisation function and is shown in experimental evaluation to increase accuracy of face detection specifically precision by reducing false positive detection's by an average of 62%. Our proposed IDM method is evaluated on the Annotated Faces In-The-Wild, Annotated Facial Landmarks In The Wild and the Face Detection Dataset and Benchmark face detection test sets and shows a high level of recall and precision when compared with previously proposed methods. Landmark localisation is evaluated on the Annotated Faces In-The-Wild and 300-W test sets, this specifically focuses on localisation accuracy from detected face bounding boxes when compared with baseline evaluations using ground truth bounding boxes. Our findings highlight only a small 0.005% maximum increase in error which is more profound for the subset of facial landmarks which border the face.

【 授权许可】

Unknown