  This paper proposes an interactive system for mobile devices controlled by
hand gestures aimed at helping people with visual impairments. This system
allows the user to interact with the device by making simple static and dynamic
hand gestures. Each gesture triggers a different action in the system, such as
object recognition, scene description or image scaling (e.g., pointing a finger
at an object will show a description of it). The system is based on a
multi-head neural network architecture, which initially detects and classifies
the gestures, and subsequently, depending on the gesture detected, performs a
second stage that carries out the corresponding action. This multi-head
architecture optimizes the resources required to perform different tasks
simultaneously, and takes advantage of the information obtained from an initial
backbone to perform different processes in a second stage. To train and
evaluate the system, a dataset with about 40k images was manually compiled and
labeled including different types of hand gestures, backgrounds (indoors and
outdoors), lighting conditions, etc. This dataset contains synthetic gestures
(whose objective is to pre-train the system in order to improve the results)
and real images captured using different mobile phones. The results obtained
and the comparison made with the state of the art show competitive results as
regards the different actions performed by the system, such as the accuracy of
classification and localization of gestures, or the generation of descriptions
for objects and scenes.
