Graham Page

Mina Bishay, Graham Page, Mohammad Mavadati

Despite the span in estimating pain from facial expressions, limited works have focused on estimating the sequence-level pain, which is reported by patients and used commonly in clinics. In this paper, we introduce a novel Statistical Relation Network, referred to as PainNet, designed for the estimation of the sequence-level pain. PainNet employs two key modules, the embedding and the relation modules, for comparing pairs of pain videos, and producing relation scores indicating if each pair belongs to the same pain category or not. At the core of the embedding module is a statistical layer mounted on the top of a RNN for extracting compact video-level features. The statistical layer is implemented as part of the deep architecture. Doing so, allows combining multiple training stages used in previous research, into a single end-to-end training stage. PainNet is trained using the episode-based training scheme, which involves comparing a query video with a set of videos representing the different pain categories. Experimental results show the benefit of using the statistical layer and the episode-based training in the proposed model. Furthermore, PainNet outperforms the state-of-the-art results on self-reported pain estimation.

Mina Bishay, Graham Page, Waleed Emad et al.

Nowadays, video ads spread through numerous online platforms, and are being watched by millions of viewers worldwide. Big brands gauge the liking and purchase intent of their new ads, by analyzing the facial responses of viewers recruited online to watch the ads from home or work. Although this approach captures naturalistic responses, it is susceptible to distractions inherent in the participants' environments, such as a movie playing on TV, a colleague speaking, or mobile notifications. Inattentive participants should get flagged and eliminated to avoid skewing the ad-testing process. In this paper we introduce an architecture for monitoring viewer attention during online ads. Leveraging two behavior analysis toolkits; AFFDEX 2.0 and SmartEye SDK, we extract low-level facial features encompassing facial expressions, head pose, and gaze direction. These features are then combined to extract high-level features that include estimated gaze on the screen plane, yawning, speaking, etc -- this enables the identification of four primary distractors; off-screen gaze, drowsiness, speaking, and unattended screen. Our architecture tailors the gaze settings according to the device type (desktop or mobile). We validate our architecture first on datasets annotated for specific distractors, and then on a real-world ad testing dataset with various distractors. The proposed architecture shows promising results in detecting distraction across both desktop and mobile devices.