Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild

Feng, Z-H, Kittler, Josef, Awais, M, Huber, Patrik and Wu, X-J (2017) Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2017), 21 - 26 July 2017, Honolulu, Hawaii.

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Abstract

We present a framework for robust face detection and landmark localisation of faces in the wild, which has been evaluated as part of ‘the 2nd Facial Landmark Localisation Competition’. The framework has four stages: face detection, bounding box aggregation, pose estimation and landmark localisation. To achieve a high detection rate, we use two publicly available CNN-based face detectors and two proprietary detectors. We aggregate the detected face bounding boxes of each input image to reduce false positives and improve face detection accuracy. A cascaded shape regressor, trained using faces with a variety of pose variations, is then employed for pose estimation and image pre-processing. Last, we train the final cascaded shape regressor for fine-grained landmark localisation, using a large number of training samples with limited pose variations. The experimental results obtained on the 300W and Menpo benchmarks demonstrate the superiority of our framework over state-of-the-art methods.

Item Type:	Conference or Workshop Item (Conference Paper)
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :	Name Email ORCID Feng, Z-H Kittler, Josef J.Kittler@surrey.ac.uk Awais, M Huber, Patrik p.huber@surrey.ac.uk Wu, X-J
Date :	27 July 2017
Copyright Disclaimer :	© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Related URLs :	Organisation
Depositing User :	Melanie Hughes
Date Deposited :	01 Jun 2017 10:48
Last Modified :	16 Jan 2019 18:52
URI:	http://epubs.surrey.ac.uk/id/eprint/841262

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