Santos, Bruno M.Pais, PedroRibeiro, Francisco M.Lima, JoséGoncalves, GilPinto, Vítor H.2020-04-302020-04-302023Santos, Bruno M.; Pais, Pedro; Ribeiro, Francisco M.; Lima, José; Goncalves, Gil; Pinto, Vítor H. (2023). Deep convolutional neural networks applied to hand keypoints estimation. In IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC). Tomar, Portugal – April 26-27, 2023. eISSN 2573-9387. p. 93-982573-9360http://hdl.handle.net/10198/21876Accurate estimation of hand shape and position is an important task in various applications, such as human-computer interaction, human-robot interaction, and virtual and augmented reality. In this paper, it is proposed a method to estimate the hand keypoints from single and colored images utilizing the pre-trained deep convolutional neural networks VGG-16 and VGG-19. The method is evaluated on the FreiHAND dataset, and the performance of the two neural networks is compared. The best results were achieved by the VGG-19, with average estimation errors of 7.40 pixels and 11.36 millimeters for the best cases of two-dimensional and three-dimensional hand keypoints estimation, respectively.engHand keypoints estimationConvolutional neural networkVGGFreiHANDconference paper10.1109/ICARSC58346.2023.101296212573-9387