Deep convolutional neural networks applied to hand keypoints estimation

Santos, Bruno M.; Pais, Pedro; Ribeiro, Francisco M.; Lima, José; Goncalves, Gil; Pinto, Vítor H.

http://hdl.handle.net/10198/21876

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Authors

Santos, Bruno M.

Pais, Pedro

Ribeiro, Francisco M.

Lima, José

Goncalves, Gil

Pinto, Vítor H.

Abstract(s)

Accurate 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.

Keywords

Hand keypoints estimation Convolutional neural network VGG FreiHAND

URI

http://hdl.handle.net/10198/21876

Citation

Santos, 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. 93-98