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Section: Partnerships and Cooperations
International Initiatives
Inria International Labs
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Associated team (2018-2020) Safe and Easy Environment for Alzheimer disease and related disorders. Inria Stars, National Taipei University of Technology Taiwan and CoBTeK team. The objective of SafEE is to develop an automated home support system, using information and communication technologies (ICT), to support the loss of autonomy and to improve the quality of life of the elderly population.
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Inria International Lab (2017-2020) Facial Expression Recognition for Health Monitoring. Coordinator: François Brémond, Antitza Dantcheva. Other partners: Chinese Academy of Sciences (CAS) (China). FER4HM aims to investigate computer vision methods for facial expression recognition in patients with Alzheimer’s disease. Most importantly though, the project seeks to be part of a paradigm shift in current health care, efficiently and cost-effectively finding objective measures to (a) assess different therapy treatments, as well to (b) enable automated human-computer interaction in remote scale health care-frameworks.
Other IIL projects
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Project title: Reliable, secure and privacy preserving multi-biometric person authentication
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Abstract: In spite of the numerous advantages of biometric recognition systems over traditional authentication systems based on PINs or passwords, these systems are vulnerable to external attacks and can leak data. Presentations attacks (PAs) – impostors who manipulate biometric samples to masquerade as other people – pose serious threats to security. Privacy concerns involve the use of personal and sensitive biometric information, as classified by the GDPR, for purposes other than those intended. Multi-biometric systems, explored extensively as a means of improving recognition reliability, also offer potential to improve PA detection (PAD) generalisation. Multi-biometric systems offer natural protection against spoofing since an impostor is less likely to succeed in fooling multiple systems simultaneously. For the same reason, previously unseen PAs are less likely to fool multi-biometric systems protected by PAD. RESPECT, a Franco-German collaborative project, explores the potential of using multi-biometrics as a means to defend against diverse PAs and improve generalisation while still preserving privacy. Central to this idea is the use of (i) biometric characteristics that can be captured easily and reliably using ubiquitous smart devices and, (ii) biometric characteristics which facilitate computationally manageable privacy preserving, homomorphic encryption.
The research focuses on characteristics readily captured with consumer-grade microphones and video cameras, specifically face, iris and voice. Further advances beyond the current state of the art involve the consideration of dynamic characteristics, namely utterance verification and lip dynamics. The core research objective is to determine which combination of biometrics characteristics gives the best biometric authentication reliability and PAD generalisation while remaining compatible with computationally efficient privacy preserving BTP schemes.
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Inserm-Inria PhD grant (October 2018- September 2021). Partners: Prof F Bartolomei Inserm UMR 1106 La Timone Hospital Marseille and M Thonnat DR Inria Stars Sophia Antipolis. The objective of the PhD thesis entitled Quantified video analysis of seizure semiology in epilepsy is to provide new automated and objective analysis and interpretation of recorded videos of patients during epilepsy seizures.
Inria Associate Teams Not Involved in an Inria International Labs
SafEE (Safe & Easy Environment)
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Title: SafEE (Safe Easy Environment) investigates technologies for the evaluation, stimulation and intervention for Alzheimer patients. The SafEE project aims at improving the safety, autonomy and quality of life of older people at risk or suffering from Alzheimer.
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International Partner (Institution - Laboratory - Researcher):
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See also: https://project.inria.fr/safee2/
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SafEE (Safe Easy Environment) investigates technologies for the evaluation, stimulation and intervention for Alzheimer patients. The SafEE project aims at improving the safety, autonomy and quality of life of older people at risk or suffering from Alzheimer's disease and related disorders. More specifically the SafEE project : 1) focuses on specific clinical targets in three domains: behavior, motricity and cognition 2) merges assessment and non pharmacological help/intervention and 3) proposes easy ICT device solutions for the end users. In this project, experimental studies will be conducted both in France (at Hospital and Nursery Home) and in Taiwan.
Declared Inria International Partners
See Taiwan, China
Participation in Other International Programs
International Initiatives
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Title: Facial expression recognition with application in health monitoring
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International Partner (Institution - Laboratory - Researcher):
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See also: https://project.inria.fr/fer4hm/
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The proposed research aims to provide computer vision methods for facial expression recognition in patients with Alzheimer’s disease. Most importantly though, the work seeks to be part of a paradigm shift in current healthcare, in efficiently and cost effectively finding objective measures to (a) assess different therapy treatments, as well as to (b) enable automated human-computer interaction in remote large-scale healthcare- frameworks. Recognizing expressions in severely demented Alzheimer's disease (AD) patients is essential, since such patients have lost a substantial amount of their cognitive capacity, and some even their verbal communication ability (e.g., aphasia). This leaves patients dependent on clinical staff to assess their verbal and non-verbal language, in order to communicate important messages, as of discomfort associated to potential complications of the AD. Such assessment classically requires the patients' presence in a clinic, and time consuming examination involving medical personnel. Thus, expression monitoring is costly and logistically inconvenient for patients and clinical staff, which hinders among others large-scale monitoring. Approaches need to cater to the challenging settings of current medical recordings, which include continuous pose variations, occlusions, camera-movements, camera-artifacts, as well as changing illumination. Additionally and importantly, the (elderly) patients exhibit generally less profound facial activities and expressions in a range of intensities and predominantly occurring in combinations (e.g., talking and smiling). Both, Inria-STARS and CAS-ICT have already initiated research activities related to the here proposed topic. While both sides have studied facial expression recognition, CAS-ICT has explored additionally the use of heart rate monitoring sensed from a webcam in this context.