Understanding face and eye visibility in front-facing cameras of smartphones used in the wild

Commodity mobile devices are now equipped with high-resolution front-facing cameras, allowing applications in biometrics (e.g., FaceID in the iPhone X), facial expression analysis, or gaze interaction. However, it is unknown how often users hold devices in a way that allows capturing their face or eyes, and how this impacts detection accuracy. We collected 25,726 in-the-wild photos, taken from the front-facing camera of smartphones as well as associated application usage logs. We found that the full face is visible about 29% of the time, and that in most cases the face is only partially visible. Furthermore, we identified an influence of users' current activity; for example, when watching videos, the eyes but not the entire face are visible 75% of the time in our dataset. We found that a state-of-the-art face detection algorithm performs poorly against photos taken from front-facing cameras. We discuss how these findings impact mobile applications that leverage face and eye detection, and derive practical implications to address state-of-the art's limitations

Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings

Conventional feature-based and model-based gaze estimation methods have proven to perform well in settings with controlled illumination and specialized cameras. In unconstrained real-world settings, however, such methods are surpassed by recent appearance-based methods due to difficulties in modeling factors such as illumination changes and other visual artifacts. We present a novel learning-based method for eye region landmark localization that enables conventional methods to be competitive to latest appearance-based methods. Despite having been trained exclusively on synthetic data, our method exceeds the state of the art for iris localization and eye shape registration on real-world imagery. We then use the detected landmarks as input to iterative model-fitting and lightweight learning-based gaze estimation methods. Our approach outperforms existing model-fitting and appearance-based methods in the context of person-independent and personalized gaze estimation

Supervised descent method (SDM) applied to accurate pupil detection in off-the-shelf eye tracking systems

The precise detection of pupil/iris center is key to estimate gaze accurately. This fact becomes specially challenging in low cost frameworks in which the algorithms employed for high performance systems fail. In the last years an outstanding effort has been made in order to apply training-based methods to low resolution images. In this paper, Supervised Descent Method (SDM) is applied to GI4E database. The 2D landmarks employed for training are the corners of the eyes and the pupil centers. In order to validate the algorithm proposed, a cross validation procedure is performed. The strategy employed for the training allows us to affirm that our method can potentially outperform the state of the art algorithms applied to the same dataset in terms of 2D accuracy. The promising results encourage to carry on in the study of training-based methods for eye tracking.Spanish Ministry of Economy,Industry and Competitiveness, contracts TIN2014-52897-R and TIN2017-84388-

A Deep Learning Approach for Robust Head Pose Independent Eye Movements Recognition from Videos

Recognizing eye movements is important for gaze behavior understanding like in human communication analysis (human-human or robot interactions) or for diagnosis (medical, reading impairments). In this paper, we address this task using remote RGB-D sensors to analyze people behaving in natural conditions. This is very challenging given that such sensors have a normal sampling rate of 30 Hz and provide low-resolution eye images (typically 36x60 pixels), and natural scenarios introduce many variabilities in illumination, shadows, head pose, and dynamics. Hence gaze signals one can extract in these conditions have lower precision compared to dedicated IR eye trackers, rendering previous methods less appropriate for the task. To tackle these challenges, we propose a deep learning method that directly processes the eye image video streams to classify them into fixation, saccade, and blink classes, and allows to distinguish irrelevant noise (illumination, low-resolution artifact, inaccurate eye alignment, difficult eye shapes) from true eye motion signals. Experiments on natural 4-party interactions demonstrate the benefit of our approach compared to previous methods, including deep learning models applied to gaze outputs

The Past, Present, and Future of Gaze-enabled Handheld Mobile Devices: {S}urvey and Lessons Learned

While first-generation mobile gaze interfaces required special-purpose hardware, recent advances in computational gaze estimation and the availability of sensor-rich and powerful devices is finally fulfilling the promise of pervasive eye tracking and eye-based interaction on off-the-shelf mobile devices. This work provides the first holistic view on the past, present, and future of eye tracking on handheld mobile devices. To this end, we discuss how research developed from building hardware prototypes, to accurate gaze estimation on unmodified smartphones and tablets. We then discuss implications by laying out 1) novel opportunities, including pervasive advertising and conducting in-the-wild eye tracking studies on handhelds, and 2) new challenges that require further research, such as visibility of the user's eyes, lighting conditions, and privacy implications. We discuss how these developments shape MobileHCI research in the future, possibly the next 20 years