text: Estimating human gaze from natural eye images only is a challenging task. Gaze direction can be defined by the pupil- and the eyeball center where the latter is unobservable in 2D images. Hence, achieving highly accurate gaze estimates is an ill-posed problem. In this paper, we introduce a novel deep neural network architecture specifically designed for the task of gaze estimation from single eye input. Instead of directly regressing two angles for the pitch and yaw of the eyeball, we regress to an intermediate pictorial representation which in turn simplifies the task of 3D gaze direction estimation. Our quantitative and qualitative results show that our approach achieves higher accuracies than the state-of-the-art and is robust to variation in gaze, head pose and image quality.
  @cite_24: We present two techniques to improve landmark localization in images from partially annotated datasets. Our primary goal is to leverage the common situation where precise landmark locations are only provided for a small data subset, but where class labels for classification or regression tasks related to the landmarks are more abundantly available. First, we propose the framework of sequential multitasking and explore it here through an architecture for landmark localization where training with class labels acts as an auxiliary signal to guide the landmark localization on unlabeled data. A key aspect of our approach is that errors can be backpropagated through a complete landmark localization model. Second, we propose and explore an unsupervised learning technique for landmark localization based on having a model predict equivariant landmarks with respect to transformations applied to the image. We show that these techniques, improve landmark prediction considerably and can learn effective detectors even when only a small fraction of the dataset has landmark labels. We present results on two toy datasets and four real datasets, with hands and faces, and report new state-of-the-art on two datasets in the wild, e.g. with only 5 of labeled images we outperform previous state-of-the-art trained on the AFLW dataset.
summary: Most similar to our contribution is the work in @cite_24 where facial landmark localization performance is improved by applying an auxiliary emotion classification loss. A key aspect to note is that their network is sequential, that is, the emotion recognition network takes only facial landmarks as input. The detected facial landmarks thus act as a manually defined representation for emotion classification, and creates a bottleneck in the full data flow. It is shown experimentally that applying such an auxiliary loss (for a different task) yields improvement over state-of-the-art results on the AFLW dataset. In our work, we learn to regress an intermediate and minimal representation for gaze direction, forming a bottleneck before the main task of regressing two angle values. Thus, an important distinction to @cite_24 is that while we employ an auxiliary loss term, it directly contributes to the task of gaze direction estimation. Furthermore, the auxiliary loss is applied as an intermediate task. We detail this further in Sec. .