2020/05/04 by Purnendu Mishra, Mishra, Purnendu, Kishor Sarawadekar +1
Computer Science · Engineering · Neuroscience · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Hand Gesture Recognition Systems #Human Pose and Action Recognition #Human-Computer Interaction (cs.HC) #Image and Video Processing (eess.IV) #Tactile and Sensory Interactions #cs.CV #cs.HC #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2005.01351
10 pages, 10 figures
openalex publication_date 2020/05/04 · arxiv created 2020/05/14 · arxiv updated 2020/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In Virtual, augmented, and mixed reality, the use of hand gestures is increasingly becoming popular to reduce the difference between the virtual and real world. The precise location of the fingertip is essential/crucial for a seamless experience. Much of the research work is based on using depth information for the estimation of the fingertips position. However, most of the work using RGB images for fingertips detection is limited to a single finger. The detection of multiple fingertips from a single RGB image is very challenging due to various factors. In this paper, we propose a deep neural network (DNN) based methodology to estimate the fingertips position. We christened this methodology as an Anchor based Fingertips Position Estimation (ABFPE), and it is a two-step process. The fingertips location is estimated using regression by computing the difference in the location of a fingertip from the nearest anchor point. The proposed framework performs the best with limited dependence on hand detection results. In our experiments on the SCUT-Ego-Gesture dataset, we achieved the fingertips detection error of 2.3552 pixels on a video frame with a resolution of 640 × 480 and about 92.98% of test images have average pixel errors of five pixels.