# EmoCAM: Toward Understanding What Drives CNN-based Emotion Recognition Source: [EmoCAM: Toward Understanding What Drives CNN-based Emotion Recognition]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:04:50.888Z Authors: Youssef Doulfoukar, Laurent Mertens, Joost Vennekens Published: 2024-07-19T13:47:02.000Z ## Abstract Convolutional Neural Networks are particularly suited for image analysis tasks, such as Image Classification, Object Recognition or Image Segmentation. Like all Artificial Neural Networks, however, they are "black box" models, and suffer from poor explainability. This work is concerned with the specific downstream task of Emotion Recognition from images, and proposes a framework that combines CAM-based techniques with Object Detection on a corpus level to better understand on which image cues a particular model, in our case EmoNet, relies to assign a specific emotion to an image. We demonstrate that the model mostly focuses on human characteristics, but also explore the pronounced effect of specific image modifications. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()