FaceX
Understanding Face Attribute Classifiers through Summary Model Explanations.
FaceX 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.
FaceX computes a region-level aggregation of model instance-level attributions, summarizing the model’s output with respect to each region of interest. Then, spatial explanations, offered through a heatmap visualization over an abstract face prototype, provide in-depth understanding of the weight of each facial region (or accessory) on the model decision. Additionally, FaceX visualizes the high-impact image patches for each region, revealing not only where the model focuses but also helping the human analyst understand why certain features are influential. This dual approach of spatial explanation (understanding where the model focuses) and appearance-oriented insights (understanding the impact of specific image patches) sets FaceX apart as a powerful tool for identifying biases in facial analysis systems and acts as a comprehensive lens, allowing practitioners, researchers, and developers to scrutinize the entire spectrum of model behavior.