Leveraging Interpretable Machines for Performance Improvement and Decision – LIMPID
Huge increase of collected data, storage capacity and computing power promote the field of Artificial Intelligence (AI) to the status of panacea to all problems. Indeed, neural networks improved the results in the fields challenging for the handcrafted algorithms previously. However, there is always a price to pay: number of its drawbacks remain unaddressed. In the real world, a decision system with AI can receive an input that is unlike anything it has seen during training. That can lead to the unpredictable behavior. Can we trust the output of such system for a particular input? In LIMPID project, we address this issue of confidence of AI output in the context of face recognition and face quality estimation in images. LIMPID concentrates on a challenge how to estimate the confidence to any response of AI algorithm. This approach can be used in a wide range of applications. LIMPID also proposes the analyses of the image features that highly contribute to the AI algorithm’s decision.
Monsieur Vincent Despiegel (IDEMIA IDENTITY & SECURITY FRANCE)
The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.
LTCI Laboratoire Traitement et Communication de l'Information
I3 institut interdisciplinaire de l'innovation
IDEMIA IDEMIA France
IDEMIA IDEMIA IDENTITY & SECURITY FRANCE
Help of the ANR 562,688 euros
Beginning and duration of the scientific project: December 2020 - 36 Months