Dr Damien Vivet, of the University of Orléans, talks about the story behind their paper ‘Access control based on gait analysis and face recognition’ page 751. Dr Damien Vivet My field focuses on multi-modal perception and its interpretation for an efficient use of sensor measurements, whatever the modality (radar, vision, laser…). I worked on various applications such as localisation and mapping for robots, autonomous or assisted driving and even for traffic analysis. Even though robotics is a very interesting research field, I wanted to apply such perception technics in a different context with the objective to assist people and analyse their behaviours. Our Letter presents a human recognition application for access control, but it has also been applied in a medical context to distinguish patients in nursing homes. I think home care applications have a big future in the current context of an aging population. For example, in France, by 2035, it is estimated that 31% of people will be over 60 years old and 13.6% will be over 75. In this situation, applications to improve home care take a very important dimension. It seems natural that detection and localisation technics used to measure activities, will be essential to increase the autonomy of these populations. This research area is wide, but we try to make a contribution by proposing descriptors in order to detect and understand human behaviour. In this article, we deal with gait, but within our research team other works are in progress on human 3D pose detection, emotion analysis and even abnormal event detection. We have proposed a people identification technic based on gait analysis and face recognition, applied and evaluated in an access control context. Our system is based on a unique low-cost camera and can be easily positioned in a room to analyse the gait of the detected people. The system is able to detect people and their faces automatically and to process both static (for faces) and dynamic (for gait) descriptors. Based on previous learning about the characteristics, the system is able to identify detected people very accurately even if the face is hidden. Existing recognition systems are mainly based on face recognition. Their main problem is when the face is hidden, for example, when people are wearing a hat, or when the detected faces are of poor quality (small image size, noisy image), detection cannot be done. Our system overcomes these limitations as, in such conditions, it can identify people using only their gait, even from the back. Of course very different clothing can also cause problems for our gait descriptor. This is why we chose to use both. In my opinion, human analysis research has a bright future. The need for behaviour analysis is increasing, whether for security or access control, home care application or even for intelligent management of building energy use in response to occupant activities. Nevertheless, we should be aware of the ethical problems of such systems. For me, the main challenge to overcome is the public's acceptance of camera sensors in a housing environment or even on the streets. Respect for private life is becoming more and more important so such visual sensors could become unsuitable. Even if we certify that no image is transmitted outside the system, people are still reticent to have such technology at home. If we want this development to happen and to be integrated in future intelligent houses, new non-intrusive imaging sensors have to be developed. Part of our group is exploring scene analysis with the objective of detecting if something that happens is abnormal or not. For this purpose, we are developing new descriptors to characterise scenes. For example, in addition to face, emotion or gait descriptors, we are currently trying to extract the 3D human pose using only a single camera, in order to easily detect and recognise activities. Moreover, we don’t just consider people, but also deal with traffic monitoring to detect infractions or accidents using a camera network.
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