CNN-Based Real-time Hand and Fingertip Recognition for the Design of a Virtual Keyboard

Li, Yanmei; Lee, Tae-Ho; Kim, Jin-Sung; Lee, Hyuk-Jae

doi:10.1109/itc-cscc52171.2021.9501471

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…Referring to the RMSE results in Table 2, GAPTCHA outperforms the Redmon et al [53] work by a big distance (34.7%). Compared to Li et al [52], our algorithm produces fewer errors (−1.79%) and is more efficient. The work by Shull et al [54] produces 0.11 RMSE, and this metric's value for the method of Manoj et al [55] increases to 0.1732.…”

Section: Discussionmentioning

confidence: 81%

“…Therefore, to compare our work with the related ones, the hand gesture detection section of GAPTCHA is compared with the similar algorithms in [52][53][54][55]. Referring to the RMSE results in Table 2, GAPTCHA outperforms the Redmon et al [52] work by a big distance (34.7%). Compared to Li et al [53], our algorithm produces fewer errors (−1.79%) and is more efficient.…”

Section: Discussionmentioning

confidence: 97%

“…To the best of our knowledge, there is no other video CAPTCHA algorithm based on hand gesture detection. Therefore, to compare our work with the related ones, the hand gesture detection section of GAPTCHA is compared with the similar algorithms in [52][53][54][55]. Referring to the RMSE results in Table 2, GAPTCHA outperforms the Redmon et al [53] work by a big distance (34.7%).…”

Section: Discussionmentioning

confidence: 97%

“…Therefore, GAPTCHA outperforms these works by 6.76% and 13.08%, respectively. [52] 0.3894 Li et al [53] 0.0603 Shull et al [54] 0.11 Manoj et al [55] 0.1732 GAPTCHA 0.0424…”

Section: Discussionmentioning

confidence: 99%

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Secure CAPTCHA by Genetic Algorithm (GA) and Multi-Layer Perceptron (MLP)

Shojae Chaeikar,

Mirzaei Asl,

Yazdanpanah

et al. 2023

Electronics

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To achieve an acceptable level of security on the web, the Completely Automatic Public Turing test to tell Computer and Human Apart (CAPTCHA) was introduced as a tool to prevent bots from doing destructive actions such as downloading or signing up. Smartphones have small screens, and, therefore, using the common CAPTCHA methods (e.g., text CAPTCHAs) in these devices raises usability issues. To introduce a reliable, secure, and usable CAPTCHA that is suitable for smartphones, this paper introduces a hand gesture recognition CAPTCHA based on applying genetic algorithm (GA) principles on Multi-Layer Perceptron (MLP). The proposed method improves the performance of MLP-based hand gesture recognition. It has been trained and evaluated on 2201 videos of the IPN Hand dataset, and MSE and RMSE benchmarks report index values of 0.0018 and 0.0424, respectively. A comparison with the related works shows a minimum of 1.79% fewer errors, and experiments produced a sensitivity of 93.42% and accuracy of 92.27–10.25% and 6.65% improvement compared to the MLP implementation. The range of the supported hand gestures can be a limit for the application of this research as a limited range may result in a vulnerable CAPTCHA. Also, the processes of training and testing require significant computational resources. In the future, we will optimize the method to run with high reliability in various illumination conditions and skin color and tone. The next development plan is to use augmented reality and create unpredictable random patterns to enhance the security of the method.

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Section: Discussionmentioning

confidence: 81%

Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 97%

“…Therefore, GAPTCHA outperforms these works by 6.76% and 13.08%, respectively. [52] 0.3894 Li et al [53] 0.0603 Shull et al [54] 0.11 Manoj et al [55] 0.1732 GAPTCHA 0.0424…”

Section: Discussionmentioning

confidence: 99%

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