The increase in elderly population is emerging as a serious social issue. The coronavirus pandemic has increased the number of elderly people suffering from depression and loneliness owing to the lack of face-to-face activities. In this study, we developed an integrated system for the human-care robot service, considering cognitive and emotional support for elderly people, and verified its stability and usefulness in the real world. We recruited 40 elderly people for an apartment testbed environment experiment and two elderly people living alone for a long time participated in the experiment at their homes. Quantitative experimental results were analyzed by comparing service success rates and user satisfaction in two different test environments to verify the stability of the service. Qualitative evaluations were also conducted through surveys and interviews to assess the usefulness of the service.

Image-based facial identity recognition has become a technology that is now used in many applications. This is because it is possible to use only a camera without the need for any other device. Besides, due to the advantage of contactless technology, it is one of the most popular certifications. However, a common recognition system is not possible if some of the face information is lost due to the user’s posture or the wearing of masks, as caused by the recent prevalent disease. In some platforms, although performance is improved through incremental updates, it is still inconvenient and inaccurate. In this paper, we propose a method to respond more actively to these situations. First, we determine whether an obscurity occurs and improve the stability by calculating the feature vector using only a significant area when the obscurity occurs. By recycling the existing recognition model, without incurring little additional costs, the results of reducing the recognition performance drop in certain situations were confirmed. Using this technique, we confirmed a performance improvement of about 1~3% in a situation where some information is lost. Although the performance is not dramatically improved, it has the big advantage that it can improve recognition performance by utilizing existing systems.