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

TL;DR

This paper introduces a four-stage framework combining face detection, bounding box aggregation, pose estimation, and landmark localisation, achieving superior accuracy in challenging real-world conditions.

Contribution

The novel framework integrates multiple detectors and a cascaded regressor pipeline, improving robustness and accuracy in facial landmark localisation in unconstrained environments.

Findings

Outperforms state-of-the-art methods on 300W and Menpo benchmarks.

Effective aggregation reduces false positives and enhances detection accuracy.

Robust to pose variations due to diverse training data.

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.