Abstract
Hand gesture recognition systems are used as an interface between computers and humans. An electronic machine capable of recognizing various types of hand gestures is easily operable using natural languages. This way of controlling a device is much more intuitive and effortless as compared to using a touch screen, manipulating a mouse or remote control, tweaking a knob or pressing a switch. This paper presents a dynamic hand gesture-based password recognition system which uses feed-forward back-propagation neural networks. The uniqueness of this system lies in the fact that it would use an ordinary low-cost webcam (built into the laptop) rather than a high-resolution Kinect camera. The advantage of using such a camera is that this software can be integrated as a security tool into everyday electronic devices such as laptops and smart phones. This can provide an additional layer of protection to security devices when combined with other security tools such as alphanumeric passwords, biometric passwords, etc. To fulfill this objective, this work uses Octave as well as a built-in laptop webcam. The obtained results show the best accuracy of 100% for the training dataset and average accuracy of 96.16% for the testing dataset. Additionally, the minimum cost function value chosen for training the samples came out as 3.556e−01.
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Pisipati, M., Puhan, A., Kumar, A., Semwal, V.B., Agrawal, H. (2020). A Dynamic Hand Gesture-Based Password Recognition System. In: Bansal, J., Gupta, M., Sharma, H., Agarwal, B. (eds) Communication and Intelligent Systems. ICCIS 2019. Lecture Notes in Networks and Systems, vol 120. Springer, Singapore. https://doi.org/10.1007/978-981-15-3325-9_2
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DOI: https://doi.org/10.1007/978-981-15-3325-9_2
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