Person Authentication by Air-Writing Using 3D Sensor and Time Order Stroke Context

Kwon

et al. 2020

Sensors

Trajectory-based writing system refers to writing a linguistic character or word in free space by moving a finger, marker, or handheld device. It is widely applicable where traditional pen-up and pen-down writing systems are troublesome. Due to the simple writing style, it has a great advantage over the gesture-based system. However, it is a challenging task because of the non-uniform characters and different writing styles. In this research, we developed an air-writing recognition system using three-dimensional (3D) trajectories collected by a depth camera that tracks the fingertip. For better feature selection, the nearest neighbor and root point translation was used to normalize the trajectory. We employed the long short-term memory (LSTM) and a convolutional neural network (CNN) as a recognizer. The model was tested and verified by the self-collected dataset. To evaluate the robustness of our model, we also employed the 6D motion gesture (6DMG) alphanumeric character dataset and achieved 99.32% accuracy which is the highest to date. Hence, it verifies that the proposed model is invariant for digits and characters. Moreover, we publish a dataset containing 21,000 digits; which solves the lack of dataset in the current research.

Section: Related Workmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Trajectory-Based Air-Writing Recognition Using Deep Neural Network and Depth Sensor

Kwon

et al. 2020

Sensors

“…On the other hand, Lewis, Lie, and Xie [7] utilized the dynamic time warping (DTW) method. Andersson and Araujo [8] explored biometric identification using the Kinect sensor to capture body movements, while Chiu, Hsieh, et al [9] tracked handwriting in the air using a 3D sensor. A wide range of applications for person identification and verification have been created utilizing these technologies, which employ diverse biometric characteristics.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-powered multimodal biometric authentication: integrating dynamic signatures and facial data for enhanced online security

Salturk,

Kahraman

2024

Neural Comput & Applic

The significant increase in online activities in the wake of recent global events has underlined the importance of biometric person authentication on digital platforms. Although many biometric devices may be used for precise biometric authentication, acquiring the necessary technology, such as 3D sensors or fingerprint scanners, can be prohibitively expensive and logistically challenging. Addressing the demands of online environments, where access to specialized hardware is limited, this paper introduces an innovative approach. In this work, by fusing static and dynamic signature data with facial data captured through regular computer cameras, a dataset of 1750 samples from 25 individuals is constructed. Deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), and temporal convolutional networks (TCN), are employed to craft a robust multi-classification model. This integration of various deep learning algorithms has demonstrated remarkable performance enhancements in biometric authentication. This research also underscores the potential of merging dynamic and static biometric features, derived from readily available sources, to yield a high-performance recognition framework. As online interactions continue to expand, the combination of various biometric modalities holds potential for enhancing the security and usability of virtual environments.

“…Person Authentication by Air-Writing Using 3D Sensor and Time Order Stroke Context [23] Rather than using a mouse or keyboard to capture person's signature, in his paper, user's signature are captured using a 3D sensor in air and then identification is verified using a novel reverse time-ordered shape context. Backward representation can effectively filter out the signatures and simplifies the process of matching as a path finding problem.…”

Section: Introductionmentioning

confidence: 99%

Fatigue Detection

Verma,

Goyal,

Kaur

2019

Preprint

The author uses two video datasets for the experiment. For the datasets, the participants are asked to sit in the driver's seat of a parked car. Then videos are captured for various mouth conditions such as resting, talking/singing, and yawning under different illumination conditions. The author keep exactly the same environmental parameters to collect videos for both datasets. The only change is the camera angle. For the first dataset, the camera is placed under the front mirror. In the second dataset, the camera is placed on the dash of the car. The author eventually describes that they achieved a higher degree of accuracy for yawning detection when the camera is placed on the dash board for the camera. [1] Martin et al. introduces the first large-scale dataset for recognizing driver activity. The data is collected in both manual and autonomous driving modes. A wide variety of information such as color, IR, depth and 3D body pose is collected from six views and categorized into 83 categories. The data is collected using five NIR-and the Kinect v2 cameras. The major challenges involves