computer-vision-3

Fingertip Detection

3260 papers • 126 benchmarks • 313 datasets

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Datasets

TI1K Dataset

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Most implemented papers

Affine transformation of virtual 3D object using 2D localization of fingertips

Mohammad Mahmudul Alam, S. Rahman•Mon Nov 30 2020

An approach of a two-stage convolutional neural network, one for the detection of hand and another for the fingertips, which outperforms the existing systems in terms of the precision of detection and interaction performance in the VR environment.

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Detection and Tracking of Fingertips for Geometric Transformation of Objects in Virtual Environment

Mohammad Mahmudul Alam, S. Rahman•Thu Oct 31 2019

This paper presents an approach of two-stage convolutional neural network (CNN) for detection of fingertips so that an interaction of the fingertips with a 3D object in the virtual environment (VR) can be established. The first-stage CNN is assigned to detect and locate the hand. Subsequently, the detected hand is cropped, resized, and fed to the second stage CNN for predicting the coordinates of fingertips. Next, a tracker is employed to track the hand continuously so that the system becomes reliable in real-time performance. The VR environments are designed to demonstrate the performance of the fingertip-based interaction system. The proposed method focuses on the geometric transformation of a virtual 3D object by using the gesture of the thumb and index finger. In particular, the distance of the thumb and index fingertips is employed to scale a 3D object in virtual environment. To realize the system, a dataset of 1000 images, named, Thumb Index 1000 (TI1K) dataset, is developed including those variations that are commonly-seen in real-life thumb and index fingers. The system is evaluated with the aid of a number of participants and virtual objects that are distinctive in nature. The proposed approach attains the desired goal and performs in real-time seamlessly to facilitate the human-computer interaction in the VR environment.

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Unified learning approach for egocentric hand gesture recognition and fingertip detection

Mohammad Mahmudul Alam, M. Islam, S. Rahman•Tue Jan 05 2021

Experimental results show that the proposed method outperforms the existing fingertip detection approaches including the Direct Regression and the Heatmap-based framework.

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Air Signing and Privacy-Preserving Signature Verification for Digital Documents

P. Sarveswarasarma, T. Sathulakjan, V. J. V. Godfrey, Thanuja D. Ambegoda•Thu May 16 2024

This paper presents a novel approach to the digital signing of electronic documents through the use of a camera-based interaction system, single-finger tracking for sign recognition, and multi commands executing hand gestures. The proposed solution, referred to as"Air Signature,"involves writing the signature in front of the camera, rather than relying on traditional methods such as mouse drawing or physically signing on paper and showing it to a web camera. The goal is to develop a state-of-the-art method for detecting and tracking gestures and objects in real-time. The proposed methods include applying existing gesture recognition and object tracking systems, improving accuracy through smoothing and line drawing, and maintaining continuity during fast finger movements. An evaluation of the fingertip detection, sketching, and overall signing process is performed to assess the effectiveness of the proposed solution. The secondary objective of this research is to develop a model that can effectively recognize the unique signature of a user. This type of signature can be verified by neural cores that analyze the movement, speed, and stroke pixels of the signing in real time. The neural cores use machine learning algorithms to match air signatures to the individual's stored signatures, providing a secure and efficient method of verification. Our proposed System does not require sensors or any hardware other than the camera.

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