Person identification from gait analysis with a depth camera at home

The aim of our project is to develop a markerless system to detect falls and evaluate the frailty of elderly people at home. In previous work, we developed an algorithm detecting falls and daily life activities based on depth images provided by Microsoft's Kinect sensor. We also developed another algorithm based on the same features for gait analysis. However, an ambient system installed at home for frailty evaluation should be able to identify the individuals that one wishes to monitor. This paper proposes a method to identify individuals based on the depth images of gait sequences. The gait sequences are detected using previously presented results on activity recognition based on Hidden Markov Models (HMMs). The visibility of the person in the sequence is assessed from the likelihood of the sequence. We propose to perform the identification of the person from her height and gait in sequences in which she walks being fully visible. The gait pattern of the person is modeled using a HMM built from features of the trajectory of the centre of mass. A specific HMM is built for each person to be identified. This approach also allows us to identify unknown individuals who do not correspond to any of the built HMMs. We test the algorithm with 10 known and 2 unknown individuals. The results show that the presented method differentiates accurately enough the unknown and known individuals, and in the last case identifies correctly the individuals. In other words, our algorithm is able to identify the person of interest among other known (family, caregivers) or unknown persons (occasional individuals).