Fine-Grained Head Pose Estimation Without Keypoints (1710.00925v5)

Abstract: Estimating the head pose of a person is a crucial problem that has a large amount of applications such as aiding in gaze estimation, modeling attention, fitting 3D models to video and performing face alignment. Traditionally head pose is computed by estimating some keypoints from the target face and solving the 2D to 3D correspondence problem with a mean human head model. We argue that this is a fragile method because it relies entirely on landmark detection performance, the extraneous head model and an ad-hoc fitting step. We present an elegant and robust way to determine pose by training a multi-loss convolutional neural network on 300W-LP, a large synthetically expanded dataset, to predict intrinsic Euler angles (yaw, pitch and roll) directly from image intensities through joint binned pose classification and regression. We present empirical tests on common in-the-wild pose benchmark datasets which show state-of-the-art results. Additionally we test our method on a dataset usually used for pose estimation using depth and start to close the gap with state-of-the-art depth pose methods. We open-source our training and testing code as well as release our pre-trained models.

• The paper presents a CNN model that uses a multi-loss framework to estimate Euler angles without relying on facial keypoints.
• Experimental results on AFLW2000 and BIWI datasets demonstrate state-of-the-art accuracy with reduced mean absolute errors.
• The method shows robust performance on low-resolution images, simplifying the pipeline for applications in surveillance and autonomous systems.

Fine-Grained Head Pose Estimation Without Keypoints

The paper "Fine-Grained Head Pose Estimation Without Keypoints" by Ruiz, Chong, and Rehg addresses the critical challenge of estimating head pose directly from image data, bypassing the traditional reliance on facial keypoints. This research proposes a novel method utilizing convolutional neural networks (CNNs) to predict Euler angles (yaw, pitch, and roll) directly, which shows superior results compared to conventional keypoint-based techniques.

Methodology and Contributions

The proposed approach abandons the fragile multi-step process of keypoint detection and 2D-3D alignment using a human head model. Instead, it employs a CNN with a multi-loss strategy that combines both binned pose classification and regression for each angle, trained on the extensive 300W-LP dataset. The key innovations include:

• Multi-Loss Network: The network predicts each Euler angle using separate losses, integrating classification and regression components. This method leverages the stable characteristics of cross-entropy loss for coarse pose estimation while refining predictions through regression.
• Generalization Across Datasets: The network demonstrates impressive generalization, trained on synthetic data and tested on real-world datasets like AFLW2000 and BIWI, achieving state-of-the-art performance.
• Handle Low-Resolution Data: The paper explores the model's performance on low-resolution images, showing that with appropriate data augmentation (random downsampling and upsampling), the network retains robustness, outperforming landmark-based methods under challenging conditions.

Numerical Results and Benchmark Comparisons

The empirical evaluation on AFLW2000 and BIWI datasets showcases the model's accuracy, with mean absolute errors significantly lower than those from keypoint-driven approaches or RGBD methods. The results on the BIWI dataset are particularly notable, approaching the precision of depth sensor techniques without utilizing additional depth information.

Theoretical and Practical Implications

The findings suggest a paradigm shift in head pose estimation tasks, emphasizing direct image-to-pose computation over reliance on facial landmarks. This approach simplifies the computational pipeline, reducing potential error sources inherent in multi-stage processes. Moreover, the robustness to varying resolutions opens up the applicability for monitoring purposes in low-resolution video feeds, enhancing usability in surveillance and autonomous vehicle systems.

Future Directions

Future research could build on this framework by expanding synthetic training datasets to cover a wider range of poses and environmental conditions. Exploring architectures that consider additional contextual cues, such as full body pose, could improve accuracy further. Integration with real-time systems would also be a valuable extension, particularly in domains where immediate feedback is critical.

In summary, this paper presents a technically sound method for head pose estimation, providing insights and results that advance the state of the art. It offers a compelling alternative to keypoint methodologies, promising broader applicability and reliability across various contexts.