PoseBias: On Dataset Bias and Task Difficulty - Is There an Optimal Camera Position for Facial Image Analysis?

Mohit Choithwani, Sneha Almeida, Bernhard Egger; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2023, pp. 3096-3104

Let's imagine you could choose the position of the camera for a particular face analysis task - where would you put it? In this work, we provide a first analysis based on synthetic training data to provide evidence that this choice is not trivial, not only dependent on the training data and different based on the task. We provide results for two major face analysis tasks, face recognition and landmark detection. For our experiments, we use a 3D Morphable Model as it provides us full control over pose, illumination, and identity to generate idealized training data. Whilst rendered images are not photorealistic we avoid any confounding factors and biases from other sources (e.g. pose bias in training data). Our results show that the optimal camera poses are near frontal but not exactly frontal and dependent on the task. By comparing the results obtained by pose-specific training set to a uniform training distribution without pose bias we show that the accuracy for both tasks not only depends on the bias in the training data but is actually dominated by the difficulty of the task depending on the particular pose.