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Article Dans Une Revue Gerontechnology Année : 2016

Emotion facial recognition by the means of automatic video analysis

Résumé

The elderly population has been growing dramatically and future predictions and estimations showcase that by 2050 the number of people over 65 years old will increase by 70%, the number of people over 80 years old will increase by 170%, outnumbering younger generations from 0-14 years. Other studies indicate that around half of the current population of over 75 year olds suffer from physical and / or mental impairments and as a result are in need of high level of care. The loss of autonomy can be delayed by maintaining an active life style, which also would lead to reduced healthcare financial costs. With the expected increase of the world elderly population, and on the other hand limited available human resources for care a question arises as ”How can we improve health care in an efficient and cost effective manner? “. Motivated by the above, we propose an approach for detecting facial expressions in Alzheimer’s patients that can be a pertinent unit in an automated assisted living system for elderly subjects. Specifically, we have collected video-data of Alzheimer’s patients in musical mnemotherapy, where even patients suffering from apathy exhibit a number of emotions and expressions. Methods: We propose a novel spatio-temporal algorithm for facial emotion and expression recognition, based on dense trajectories, bag of features and support vector machine classification. We compare the proposed algorithm to a facial-landmark-based algorithm concerning signal displacement of tracked points within the face. Challenging for these algorithms (and specifically for the latter) has been the unconstrained setting involving Alzheimer’s patients in musical mnemotherapy and interviews. We have recorded data at the Alzheimer’s center Fondation G.S.F Jean Louis Noisiez in Biot from multiple therapy-sessions that serves to validate our method. The proposed system differentiates between four different facial expressions, namely neutral, smile, sad and talking. One expected benefit for Alzheimer’s patients is that positive emotions and their cause could be determined and replicated in order to increase life standard for such patients, which also brings to the fore a delay in the development of Alzheimer’s.
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Dates et versions

hal-01446930 , version 1 (09-03-2017)

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  • HAL Id : hal-01446930 , version 1

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Antitza Dantcheva, Piotr Bilinski, Jean Claude Broutart, Philippe Robert, Francois Bremond. Emotion facial recognition by the means of automatic video analysis. Gerontechnology, 2016. ⟨hal-01446930⟩
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