Ismahane Cheheb

Papers

Total Citations

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About

Ismahane Cheheb is a researcher whose work lies at the intersection of computer vision, pattern recognition, and affective computing. Her most cited contribution, "Emotion recognition from scrambled facial images via many graph embedding" (2017, 62 citations), introduces a novel approach to decoding human emotions from partially obscured or scrambled facial data. This work is particularly significant for real-world applications where facial features may be occluded or degraded, such as in surveillance, human-computer interaction, and assistive technologies. By leveraging graph embedding techniques, Cheheb demonstrates how to preserve discriminative information even when input data is incomplete, advancing the robustness of emotion recognition systems. Her research has practical implications for improving machine empathy and security systems. With over 60 citations on this key paper alone, Cheheb's contributions are recognized for pushing the boundaries of how machines interpret subtle, non-verbal human cues, making her a notable figure in the field of affective computing and biometrics.