Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Liveliness detection acts as a safe guard against spoofing attacks. Most of the researchers used vision based techniques to detect liveliness of the user, but they are highly sensitive to illumination effects. Therefore it is very hard to design a system, which will work robustly under all circumstances. Literature shows that most of the research utilize eye blink or mouth movement to detect the liveliness, while the other group used face texture to distinguish beten real and imposter. The classification results of all these approaches decreases drastically in variable light conditions. Hence in this paper  are introducing fuzzy expert system which is sufficient enough to handle most of the cases comes in real time.  have used two testing parameters, (a) under bad illumination and (b) less movement in eyes and mouth in case of real user to evaluate the performance of the system. The system is behaving ll in all, while in first case its False Rejection Rate (FRR) is 0.28, and in second case its FRR is 0.4.
['Avinash Kumar Singh', 'Piyush Joshi', 'Gora Chand Nandi']
I am writing a research paper on a idea Development of a Fuzzy Expert System based Liveliness Detection Scheme for Biometric Authentication
[{"from": "human", "value": "I am writing a research paper on a idea Development of a Fuzzy Expert System based Liveliness Detection Scheme for Biometric Authentication"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Liveliness detection acts as a safe guard against spoofing attacks. Most of the researchers used vision based techniques to detect liveliness of the user, but they are highly sensitive to illumination effects. Therefore it is very hard to design a system, which will work robustly under all circumstances. Literature shows that most of the research utilize eye blink or mouth movement to detect the liveliness, while the other group used face texture to distinguish beten real and imposter. The classification results of all these approaches decreases drastically in variable light conditions. Hence in this paper  are introducing fuzzy expert system which is sufficient enough to handle most of the cases comes in real time.  have used two testing parameters, (a) under bad illumination and (b) less movement in eyes and mouth in case of real user to evaluate the performance of the system. The system is behaving ll in all, while in first case its False Rejection Rate (FRR) is 0.28, and in second case its FRR is 0.4."}]