Unveiling the Human-like Similarities of Automatic Facial Expression Recognition: An Empirical Exploration through Explainable AI

• URL: http://arxiv.org/abs/2401.11835v3
• Date: Tue, 3 Sep 2024 07:39:39 GMT
• Title: Unveiling the Human-like Similarities of Automatic Facial Expression Recognition: An Empirical Exploration through Explainable AI
• Abstract summary: This study aims to explore the similarity between deep neural networks and human perception by comparing twelve different networks. We employ an innovative global explainable AI method to generate heatmaps, revealing crucial facial regions for the twelve networks trained on six facial expressions. Findings suggest limited alignment between human and AI facial expression recognition, with network architectures influencing the similarity.
• Score: 0.562479170374811
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Facial expression recognition is vital for human behavior analysis, and deep learning has enabled models that can outperform humans. However, it is unclear how closely they mimic human processing. This study aims to explore the similarity between deep neural networks and human perception by comparing twelve different networks, including both general object classifiers and FER-specific models. We employ an innovative global explainable AI method to generate heatmaps, revealing crucial facial regions for the twelve networks trained on six facial expressions. We assess these results both quantitatively and qualitatively, comparing them to ground truth masks based on Friesen and Ekman's description and among them. We use Intersection over Union (IoU) and normalized correlation coefficients for comparisons. We generate 72 heatmaps to highlight critical regions for each expression and architecture. Qualitatively, models with pre-trained weights show more similarity in heatmaps compared to those without pre-training. Specifically, eye and nose areas influence certain facial expressions, while the mouth is consistently important across all models and expressions. Quantitatively, we find low average IoU values (avg. 0.2702) across all expressions and architectures. The best-performing architecture averages 0.3269, while the worst-performing one averages 0.2066. Dendrograms, built with the normalized correlation coefficient, reveal two main clusters for most expressions: models with pre-training and models without pre-training. Findings suggest limited alignment between human and AI facial expression recognition, with network architectures influencing the similarity, as similar architectures prioritize similar facial regions.

Related papers

• Inter-X++: A Comprehensive Benchmark for Multimodal Human-Human Interaction Analysis [55.78822230833247]
  We present Inter-X++, a comprehensive and large-scale benchmark designed to empower versatile HHI analysis.<n>Inter-X++ provides 11,388 high-fidelity interaction sequences and over 8.1M frames, featuring precise whole-body movements and detailed finger articulations.<n>We propose OpenHHI, a single and unified HHI representation and modeling framework that jointly optimize interaction reconstruction and semantic understanding.
  arXiv  Detail & Related papers  (2026-08-20T17:51:48Z)
• AlignFace: Human-Aligned Face Similarity Metric with Interpretable Concept Relations [13.228617286387887]
  We introduce the FACETS dataset and propose AlignFace, an interpretable, human-aligned, face similarity metric.<n>Experiments found AlignFace significantly improves alignment with human subpopulation perceptions compared to baseline metrics.
  arXiv  Detail & Related papers  (2026-08-14T09:36:28Z)
• Learning Diversified Feature Representations for Facial Expression Recognition in the Wild [97.14064057840089]
  We propose a mechanism to diversify the features extracted by CNN layers of state-of-the-art facial expression recognition architectures. Experimental results on three well-known facial expression recognition in-the-wild datasets, AffectNet, FER+, and RAF-DB, show the effectiveness of our method.
  arXiv  Detail & Related papers  (2022-10-17T19:25:28Z)
• CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition [80.07590100872548]
  We propose Contrastive Inhibitory Adaptati On (CIAO), a mechanism that adapts the last layer of facial encoders to depict specific affective characteristics on different datasets. CIAO presents an improvement in facial expression recognition performance over six different datasets with very unique affective representations.
  arXiv  Detail & Related papers  (2022-08-10T15:46:05Z)
• Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Classification and Prediction [0.0]
  This paper proposes a hybrid model for Facial Expression recognition, which comprises a Deep Convolutional Neural Network (DCNN) and Haar Cascade deep learning architectures. The objective is to classify real-time and digital facial images into one of the seven facial emotion categories considered. The experimental results show a significantly improved classification performance compared to state-of-the-art experiments and research.
  arXiv  Detail & Related papers  (2022-06-19T23:43:41Z)
• Affect-DML: Context-Aware One-Shot Recognition of Human Affect using Deep Metric Learning [29.262204241732565]
  Existing methods assume that all emotions-of-interest are given a priori as annotated training examples. We conceptualize one-shot recognition of emotions in context -- a new problem aimed at recognizing human affect states in finer particle level from a single support sample. All variants of our model clearly outperform the random baseline, while leveraging the semantic scene context consistently improves the learnt representations.
  arXiv  Detail & Related papers  (2021-11-30T10:35:20Z)
• Multi-Branch Deep Radial Basis Function Networks for Facial Emotion Recognition [80.35852245488043]
  We propose a CNN based architecture enhanced with multiple branches formed by radial basis function (RBF) units. RBF units capture local patterns shared by similar instances using an intermediate representation. We show it is the incorporation of local information what makes the proposed model competitive.
  arXiv  Detail & Related papers  (2021-09-07T21:05:56Z)
• Multi-modal Affect Analysis using standardized data within subjects in the Wild [8.05417723395965]
  We introduce the affective recognition method focusing on facial expression (EXP) and valence-arousal calculation. Our proposed framework can improve estimation accuracy and robustness effectively.
  arXiv  Detail & Related papers  (2021-07-07T04:18:28Z)
• A Multi-resolution Approach to Expression Recognition in the Wild [9.118706387430883]
  We propose a multi-resolution approach to solve the Facial Expression Recognition task. We ground our intuition on the observation that often faces images are acquired at different resolutions. To our aim, we use a ResNet-like architecture, equipped with Squeeze-and-Excitation blocks, trained on the Affect-in-the-Wild 2 dataset.
  arXiv  Detail & Related papers  (2021-03-09T21:21:02Z)
• DRG: Dual Relation Graph for Human-Object Interaction Detection [65.50707710054141]
  We tackle the challenging problem of human-object interaction (HOI) detection. Existing methods either recognize the interaction of each human-object pair in isolation or perform joint inference based on complex appearance-based features. In this paper, we leverage an abstract spatial-semantic representation to describe each human-object pair and aggregate the contextual information of the scene via a dual relation graph.
  arXiv  Detail & Related papers  (2020-08-26T17:59:40Z)
• Symbiotic Adversarial Learning for Attribute-based Person Search [86.7506832053208]
  We present a symbiotic adversarial learning framework, called SAL.Two GANs sit at the base of the framework in a symbiotic learning scheme. Specifically, two different types of generative adversarial networks learn collaboratively throughout the training process.
  arXiv  Detail & Related papers  (2020-07-19T07:24:45Z)
• Seeing eye-to-eye? A comparison of object recognition performance in humans and deep convolutional neural networks under image manipulation [0.0]
  This study aims towards a behavioral comparison of visual core object recognition performance between humans and feedforward neural networks. Analyses of accuracy revealed that humans not only outperform DCNNs on all conditions, but also display significantly greater robustness towards shape and most notably color alterations.
  arXiv  Detail & Related papers  (2020-07-13T10:26:30Z)
• Joint Deep Learning of Facial Expression Synthesis and Recognition [97.19528464266824]
  We propose a novel joint deep learning of facial expression synthesis and recognition method for effective FER. The proposed method involves a two-stage learning procedure. Firstly, a facial expression synthesis generative adversarial network (FESGAN) is pre-trained to generate facial images with different facial expressions. In order to alleviate the problem of data bias between the real images and the synthetic images, we propose an intra-class loss with a novel real data-guided back-propagation (RDBP) algorithm.
  arXiv  Detail & Related papers  (2020-02-06T10:56:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.