Integrated Deep Model for Face Detection and Landmark Localization From “In The Wild” Images
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 Int...
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8540344/ |
| _version_ | 1818415331755950080 |
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| author | Gary Storey Ahmed Bouridane Richard Jiang |
| author_facet | Gary Storey Ahmed Bouridane Richard Jiang |
| author_sort | Gary Storey |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T11:33:18Z |
| format | Article |
| id | doaj.art-b0eeeca4938a4d69ae20771aac6b3cf2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T11:33:18Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b0eeeca4938a4d69ae20771aac6b3cf22022-12-21T23:03:11ZengIEEEIEEE Access2169-35362018-01-016744427445210.1109/ACCESS.2018.28822278540344Integrated Deep Model for Face Detection and Landmark Localization From “In The Wild” ImagesGary Storey0https://orcid.org/0000-0001-5492-0433Ahmed Bouridane1Richard Jiang2Department of Computer and Information Sciences, Northumbria University, Newcastle Upon Tyne, U.K.Department of Computer and Information Sciences, Northumbria University, Newcastle Upon Tyne, U.K.Department of Computer and Information Sciences, Northumbria University, Newcastle Upon Tyne, U.K.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.https://ieeexplore.ieee.org/document/8540344/Computer visionface detectionmachine learning |
| spellingShingle | Gary Storey Ahmed Bouridane Richard Jiang Integrated Deep Model for Face Detection and Landmark Localization From “In The Wild” Images IEEE Access Computer vision face detection machine learning |
| title | Integrated Deep Model for Face Detection and Landmark Localization From “In The Wild” Images |
| title_full | Integrated Deep Model for Face Detection and Landmark Localization From “In The Wild” Images |
| title_fullStr | Integrated Deep Model for Face Detection and Landmark Localization From “In The Wild” Images |
| title_full_unstemmed | Integrated Deep Model for Face Detection and Landmark Localization From “In The Wild” Images |
| title_short | Integrated Deep Model for Face Detection and Landmark Localization From “In The Wild” Images |
| title_sort | integrated deep model for face detection and landmark localization from x201c in the wild x201d images |
| topic | Computer vision face detection machine learning |
| url | https://ieeexplore.ieee.org/document/8540344/ |
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