Dynamic-Hand-Gesture Authentication Dataset and Benchmark

Liu, Chang; Yang, Yulin; Liu, Xingyan; Fang, Linpu; Kang, Wenxiong

doi:10.1109/tifs.2020.3036218

Cited by 18 publications

(3 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experiment, which used IITD Iris database and the XM2VTSDB Face database, took 30 milliseconds to complete authentication. Dynamic hand-gesture has huge potential value with advantage of templatereplaceability and safety as suggested by Liu et al [15]. SCUT-DHGA is a vast Dynamic Hand Gesture Authentication dataset that contains over 1.86X1000000 frames in both depth and color modalities, as well as 29,160 dynamic hand gesture video sequences, all collected from 193 volunteers.…”

Section: E a R L Y B I R Dmentioning

confidence: 99%

See 1 more Smart Citation

Process of Fingerprint Authentication using Cancelable Biohashed Template

K R,

K R

2024

csci

View full text Add to dashboard Cite

Template protection using cancelable biometrics prevents data loss and hacking stored templates, by providing considerable privacy and security. Hashing and salting techniques are used to build resilient systems. Salted password method is employed to protect passwords against different types of attacks namely brute-force attack, dictionary attack, rainbow table attacks. Salting claims that random data can be added to input of hash function to ensure unique output. Hashing salts are speed bumps in an attacker’s road to breach user’s data. Research proposes a contemporary two factor authenticator called Biohashing. Biohashing procedure is implemented by recapitulated inner product over a pseudo random number generator key, as well as fingerprint features that are a network of minutiae. Cancelable template authentication used in fingerprint-based sales counter accelerates payment process. Fingerhash is code produced after applying biohashing on fingerprint. Fingerhash is a binary string procured by choosing individual bit of sign depending on a preset threshold. Experiment is carried using benchmark FVC 2002 DB1 dataset. Authentication accuracy is found to be nearly 97\%. Results compared with state-of art approaches finds promising.

show abstract

Section: E a R L Y B I R Dmentioning

confidence: 99%

“…Jaccard Index, also known as Jaccard Similarity coefficient, is a statistic used in understanding similarities between sample sets is given by equation (15). Measurement emphasizes similarity between finite sample sets and is formally defined as size…”

Section: E a R L Y B I R Dmentioning

confidence: 99%

Process of Fingerprint Authentication using Cancelable Biohashed Template

K R,

K R

2024

csci

View full text Add to dashboard Cite

show abstract

“…Gait authentication uses the whole body, so it is difficult to change the movement, but hand gestures are very convenient because it is easy to change the movement pattern. There are previous studies such as [9,10] for authentication using hand gestures, but the number of examples is small compared to authentication based on other behavioral characteristics [11]. Only color images are used for hand gesture authentication in reference [9], and only depth images are used in the method in reference [10].…”

Section: Introductionmentioning

confidence: 99%

Hand authentication from RGB-D video based on deep neural network

Ryogo¹,

Sasaki²,

Tsumura³

et al. 2022

View full text Add to dashboard Cite

In recent years, behavioral biometrics authentication, which uses the habit of behavioral characteristics for personal authentication, has attracted attention as an authentication method with higher security since behavioral biometrics cannot mimic as fingerprint and face authentications. As the behavioral biometrics, many researches were performed on voiceprints. However, there are few authentication technologies that utilize the habits of hand and finger movements during hand gestures. Only either color images or depth images are used for hand gesture authentication in the conventional methods.In the research, therefore, we propose to find individual habits from RGB-D images of finger movements and create a personal authentication system. 3D CNN, which is a deep learning-based network, is used to extract individual habits. An Fmeasure of 0.97 is achieved when rock-paper-scissors are used as the authentication operation. An F-measure of 0.97 is achieved when the disinfection operation is used. These results show the effectiveness of using RGB-D video for personal authentication.

show abstract