  Recommendation systems based on image recognition could prove a vital tool in
enhancing the experience of museum audiences. However, for practical systems
utilizing wearable cameras, a number of challenges exist which affect the
quality of image recognition. In this pilot study, we focus on recognition of
museum collections by using a wearable camera in three different museum spaces.
We discuss the application of wearable cameras, and the practical and technical
challenges in devising a robust system that can recognize artworks viewed by
the visitors to create a detailed record of their visit. Specifically, to
illustrate the impact of different kinds of museum spaces on image recognition,
we collect three training datasets of museum exhibits containing variety of
paintings, clocks, and sculptures. Subsequently, we equip selected visitors
with wearable cameras to capture artworks viewed by them as they stroll along
exhibitions. We use Convolutional Neural Networks (CNN) which are pre-trained
on the ImageNet dataset and fine-tuned on each of the training sets for the
purpose of artwork identification. In the testing stage, we use CNNs to
identify artworks captured by the visitors with a wearable camera. We analyze
the accuracy of their recognition and provide an insight into the applicability
of such a system to further engage audiences with museum exhibitions.
