Humans routinely extract important information from images and videos, relying on their gaze. In contrast, computational systems still have difficulty annotating important visual information in a human-like manner, in part because human gaze is often not included in the modeling process. Human input is also particularly relevant for processing and interpreting affective visual information. To address this challenge, we captured human gaze, spoken language, and facial expressions simultaneously in an experiment with visual stimuli characterized by subjective and affective content. Observers described the content of complex emotional images and videos depicting positive and negative scenarios and also their feelings about the imagery being viewed. We explore patterns of these modalities, for example by comparing the affective nature of participant-elicited linguistic tokens with image valence. Additionally, we expand a framework for generating automatic alignments between the gaze and spoken language modalities for visual annotation of images. Multimodal alignment is challenging due to their varying temporal offset. We explore alignment robustness when images have affective content and whether image valence influences alignment results. We also study if word frequency-based filtering impacts results, with both the unfiltered and filtered scenarios performing better than baseline comparisons, and with filtering resulting in a substantial decrease in alignment error rate. We provide visualizations of the resulting annotations from multimodal alignment. This work has implications for areas such as image understanding, media accessibility, and multimodal data fusion.
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Using Co-Captured Face, Gaze, and Verbal Reactions to Images of Varying Emotional Content for Analysis and Semantic Alignment
Analyzing different modalities of expression can provide insights into the ways that humans interpret, label, and react to images. Such insights have the potential not only to advance our understanding of how humans coordinate these expressive modalities but also to enhance existing methodologies for common AI tasks such as image annotation and classification. We conducted an experiment that co-captured the facial expressions, eye movements, and spoken language data that observers produce while examining images of varying emotional content and responding to description-oriented vs. affect-oriented questions about those images. We analyzed the facial expressions produced by the observers in order to determine the connection between those expressions and an image's emotional content. We also explored the relationship between the valence of an image and the verbal responses to that image, and how that relationship relates to the nature of the prompt, using low-level lexical features and more complex affective features extracted from the observers' verbal responses. Finally, in order to integrate this multimodal data, we extended an existing bitext alignment framework to create meaningful pairings between narrated observations about images and the image regions indicated by eye movement data. The resulting annotations of image regions with words from observers' responses demonstrate the potential of bitext alignment for multimodal data integration and, from an application perspective, for annotation of open-domain images. In addition, we found that while responses to affect-oriented questions appear useful for image understanding, their holistic nature seems less helpful for image region annotation.
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- Award ID(s):
- 1559889
- NSF-PAR ID:
- 10042952
- Date Published:
- Journal Name:
- The AAAI-17 Workshop on Human-Aware Artificial Intelligence
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Most of the research in the field of affective computing has focused on detecting and classifying human emotions through electroencephalogram (EEG) or facial expressions. Designing multimedia content to evoke certain emotions has been largely motivated by manual rating provided by users. Here we present insights from the correlation of affective features between three modalities namely, affective multimedia content, EEG, and facial expressions. Interestingly, low-level Audio-visual features such as contrast and homogeneity of the video and tone of the audio in the movie clips are most correlated with changes in facial expressions and EEG. We also detect the regions associated with the human face and the brain (in addition to the EEG frequency bands) that are most representative of affective responses. The computational modeling between the three modalities showed a high correlation between features from these regions and user-reported affective labels. Finally, the correlation between different layers of convolutional neural networks with EEG and Face images as input provides insights into human affection. Together, these findings will assist in (1) designing more effective multimedia contents to engage or influence the viewers, (2) understanding the brain/body bio-markers of affection, and (3) developing newer brain-computer interfaces as well as facial-expression-based algorithms to read emotional responses of the viewers.
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We introduce caption-guided face recognition (CGFR) as a new framework to improve the performance of commercial-off-the-shelf (COTS) face recognition (FR) systems. In contrast to combining soft biometrics (e.g., facial marks, gender, and age) with face images, in this work, we use facial descriptions provided by face examiners as a piece of auxiliary information. However, due to the heterogeneity of the modalities, improving the performance by directly fusing the textual and facial features is very challenging, as both lie in different embedding spaces. In this paper, we propose a contextual feature aggregation module (CFAM) that addresses this issue by effectively exploiting the fine-grained word-region interaction and global image-caption association. Specifically, CFAM adopts a self-attention and a cross-attention scheme for improving the intra-modality and inter-modality relationship between the image and textual features. Additionally, we design a textual feature refinement module (TFRM) that refines the textual features of the pre-trained BERT encoder by updating the contextual embeddings. This module enhances the discriminative power of textual features with a crossmodal projection loss and realigns the word and caption embeddings with visual features by incorporating a visualsemantic alignment loss. We implemented the proposed CGFR framework on two face recognition models (Arc- Face and AdaFace) and evaluated its performance on the Multimodal CelebA-HQ dataset. Our framework improves the performance of ArcFace from 16.75% to 66.83% on TPR@FPR=1e-4 in the 1:1 verification protocol.more » « less
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Abstract Faces are among the most important visual stimuli that humans perceive in everyday life. While extensive literature has examined emotional processing and social evaluations of faces, most studies have examined either topic using unimodal approaches. In this review, we promote the use of multimodal cognitive neuroscience approaches to study these processes, using two lines of research as examples: ambiguity in facial expressions of emotion and social trait judgment of faces. In the first set of studies, we identified an event‐related potential that signals emotion ambiguity using electroencephalography and we found convergent neural responses to emotion ambiguity using functional neuroimaging and single‐neuron recordings. In the second set of studies, we discuss how different neuroimaging and personality‐dimensional approaches together provide new insights into social trait judgments of faces. In both sets of studies, we provide an in‐depth comparison between neurotypicals and people with autism spectrum disorder. We offer a computational account for the behavioral and neural markers of the different facial processing between the two groups. Finally, we suggest new practices for studying the emotional processing and social evaluations of faces. All data discussed in the case studies of this review are publicly available.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
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