###### Abstract  
The provision of information encourages people to visit cultural sites more often. Exploiting the great potential of using smartphone cameras and egocentric vision, we describe the development of a robust artwork recognition algorithm to assist users when visiting an art space. The algorithm recognizes artworks under any physical museum conditions, as well as camera point of views, making it suitable for different use scenarios towards an enhanced visiting experience. The algorithm was developed following a multiphase approach, including requirements gathering, experimentation in a virtual environment, development of the algorithm in real environment conditions, implementation of a demonstration smartphone app for artwork recognition and provision of assistive information, and its evaluation. During the algorithm development process, a convolutional neural network (CNN) model was trained for automatic artwork recognition using data collected in an art gallery, followed by extensive evaluations related to the parameters that may affect recognition accuracy, while the optimized algorithm was also evaluated through a dedicated app by a group of volunteers with promising results. The overall algorithm design and evaluation adopted for this work can also be applied in numerous applications, especially in cases where the algorithm performance under varying conditions and end-user satisfaction are critical factors. egocentric vision; artwork recognition; visiting experience; smartphone app; deep learning +
Footnote †: journal: _Algorithms_  
definition  
displays artworks, including paintings, sculptures, ceramics and multimedia art, from the 19 th and 20 th centuries created by Cypriot artists. The work involves the recognition of the State Gallery's artworks from the visitor's point of view, thus enabling: (a) the provision of additional information about the artwork to the visitor, and (b) the registration of the artworks that the visitor is paying more attention to as a means of automating the process of visitor experience evaluation studies [5]. Going one step further from previous research efforts in egocentric artwork recognition, we concentrated on the development of a robust recognition algorithm able to cover the needs of an art gallery and enhance the overall visiting experience. For this purpose, a systematic approach was followed, which was subdivided into five phases: (i) the collection of requirements through on-site visits and discussions with curators and museum professionals, (ii) an investigation in a virtual environment, (iii) the development of the artwork recognition algorithm and its testing in real setting, (iv) the design and implementation of a smartphone app for testing the developed algorithm and (v) an evaluation concerning both the user experience and recognition accuracy (Figure 1). The artwork recognition in a museum was initially tested in a virtual environment as a proof of concept. Following the preliminary investigation in the virtual environment, an artwork identification algorithm was developed. To train the algorithm, a first-person dataset was created based on the artworks of a specific Gallery's hall, where the rear camera of a midrange smartphone placed on the chest of the wearer with a forward-facing direction to replicate the point of view of a typical visitor of the Gallery. A series of quantitative performance evaluation experiments were run in order to assess the performance of the artwork recognition algorithms. The most efficient recognition model was embedded in a smartphone app that automatically recognized and displayed information about the artwork that the visitor was looking at. The evaluation involved 33 volunteers who tested the app and answered an online questionnaire regarding user experience. The hierarchical algorithm design and evaluation pipeline adopted in this work, aimed to satisfy the need of implementing a highly accurate algorithm that fulfilled the expectations of the end users.