FaceX: Understanding Face Attribute Classifiers through Summary Model Explanations

• URL: http://arxiv.org/abs/2412.07313v1
• Date: Tue, 10 Dec 2024 08:50:41 GMT
• Title: FaceX: Understanding Face Attribute Classifiers through Summary Model Explanations
• Authors: Ioannis Sarridis, Christos Koutlis, Symeon Papadopoulos, Christos Diou,
• Abstract summary: FaceX is the first method that provides a comprehensive understanding of face attribute classifiers through summary model explanations.<n>Specifically, FaceX leverages the presence of distinct regions across all facial images to compute a region-level aggregation of model activations.<n>Beyond spatial explanations, FaceX enhances interpretability by visualizing specific image patches with the highest impact on the model's decisions for each facial region within a test benchmark.
• Score: 19.140362626182856
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: EXplainable Artificial Intelligence (XAI) approaches are widely applied for identifying fairness issues in Artificial Intelligence (AI) systems. However, in the context of facial analysis, existing XAI approaches, such as pixel attribution methods, offer explanations for individual images, posing challenges in assessing the overall behavior of a model, which would require labor-intensive manual inspection of a very large number of instances and leaving to the human the task of drawing a general impression of the model behavior from the individual outputs. Addressing this limitation, we introduce FaceX, the first method that provides a comprehensive understanding of face attribute classifiers through summary model explanations. Specifically, FaceX leverages the presence of distinct regions across all facial images to compute a region-level aggregation of model activations, allowing for the visualization of the model's region attribution across 19 predefined regions of interest in facial images, such as hair, ears, or skin. Beyond spatial explanations, FaceX enhances interpretability by visualizing specific image patches with the highest impact on the model's decisions for each facial region within a test benchmark. Through extensive evaluation in various experimental setups, including scenarios with or without intentional biases and mitigation efforts on four benchmarks, namely CelebA, FairFace, CelebAMask-HQ, and Racial Faces in the Wild, FaceX demonstrates high effectiveness in identifying the models' biases.

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