Tom Gedeon

Surbhi Madan, Shreya Ghosh, Ramanathan Subramanian et al.

A person's gaze offers valuable insights into their focus of attention, level of social engagement, and confidence. In this work, we investigate how contextual cues combined with visual scene and facial information can be effectively utilized to predict and interpret social gaze patterns during conversational interactions. We introduce CSGaze, a context aware multimodal approach that leverages facial, scene information as complementary inputs to enhance social gaze pattern prediction from multi-person images. The model also incorporates a fine-grained attention mechanism centered on the principal speaker, which helps in better modeling social gaze dynamics. Experimental results show that CSGaze performs competitively with state-of-the-art methods on GP-Static, UCO-LAEO and AVA-LAEO. Our findings highlight the role of contextual cues in improving social gaze prediction. Additionally, we provide initial explainability through generated attention scores, offering insights into the model's decision-making process. We also demonstrate our model's generalizability by testing our model on open set datasets that demonstrating its robustness across diverse scenarios.

Yixiao Chen, Yue Yao, Ruining Yang et al.

This article investigates the critical issue of dataset bias in medical imaging, with a particular emphasis on racial disparities caused by uneven population distribution in dataset collection. Our analysis reveals that medical segmentation datasets are significantly biased, primarily influenced by the demographic composition of their collection sites. For instance, Scanning Laser Ophthalmoscopy (SLO) fundus datasets collected in the United States predominantly feature images of White individuals, with minority racial groups underrepresented. This imbalance can result in biased model performance and inequitable clinical outcomes, particularly for minority populations. To address this challenge, we propose a novel training set search strategy aimed at reducing these biases by focusing on underrepresented racial groups. Our approach utilizes existing datasets and employs a simple greedy algorithm to identify source images that closely match the target domain distribution. By selecting training data that aligns more closely with the characteristics of minority populations, our strategy improves the accuracy of medical segmentation models on specific minorities, i.e., Black. Our experimental results demonstrate the effectiveness of this approach in mitigating bias. We also discuss the broader societal implications, highlighting how addressing these disparities can contribute to more equitable healthcare outcomes.

Ruiqi Chen, Atiqul Islam, Tom Gedeon et al.

Pupillary responses (PR) change differently for different types of stimuli. This study aims to check whether observers PR can recognise real and posed smiles from a set of smile images and videos. We showed the smile images and smile videos stimuli to observers, and recorded their pupillary responses considering four different situations, namely paired videos, paired images, single videos, and single images. When the same smiler was viewed by observers in both real and posed smile forms, we refer them as paired; otherwise we use the term single. The primary analysis on pupil data revealed that the differences of pupillary response between real and posed smiles are more significant in case of paired videos compared to others. This result is found from timeline analysis, KS-test, and ANOVA test. Overall, our model can recognise real and posed smiles from observers pupillary responses instead of smilers responses. Our research will be applicable in affective computing and computer-human interaction for measuring emotional authenticity.

Yang Liu, Tom Gedeon, Sabrina Caldwell et al.

Emotion recognition based on physiological signals is a hot topic and has a wide range of applications, like safe driving, health care and creating a secure society. This paper introduces a physiological dataset PAFEW, which is obtained using movie clips from the Acted Facial Expressions in the Wild (AFEW) dataset as stimuli. To establish a baseline, we use the electrodermal activity (EDA) signals in this dataset and extract 6 features from each signal series corresponding to each movie clip to recognize 7 emotions, i.e., Anger, Disgust, Fear, Happy, Surprise, Sad and Neutral. Overall, 24 observers participated in our collection of the training set, including 19 observers who participated in only one session watching 80 videos from 7 classes and 5 observers who participated multiple times and watched all the videos. All videos were presented in an order balanced fashion. Leave-one-observer-out was employed in this classification task. We report the classification accuracy of our baseline, a three-layer network, on this initial training set while training with signals from all participants, only single participants and only multiple participants. We also investigate the recognition accuracy of grouping the dataset by arousal or valence, which achieves 68.66% and 72.72% separately. Finally, we provide a two-step network. The first step is to classify the features into high/low arousal or positive/negative valence by a network. Then the arousal/valence middle output of the first step is concatenated with feature sets as input of the second step for emotion recognition. We found that adding arousal or valence information can help to improve the classification accuracy. In addition, the information of positive/negative valence boosts the classification accuracy to a higher degree on this dataset.