Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Face recognition is a widely used biometric technology because it is both user friendly and more convenient to use than other biometric approaches. Hover, naive face recognition systems that do not support any type of liveness detection can be easily spoofed using just a photograph of a valid user. Face liveness detection is a key issue in the field of security systems that use a camera. Unfortunately, it is not easy to detect face liveness using existing methods, assuming that there are print failures and overall image blur. With the development of display devices and image capturing technology, it is possible to reproduce face images similar to real faces. Therefore, the number of attacks using a photograph or video displayed on a screen rather than paper will increase. In this study,  compare test results using live faces and high-definition face videos from light-emitting diode (LED) display devices and analyze the changes in face recognition performance according to the lighting direction. Experimental results show that there is no significant difference beten live faces and not live faces under good lighting conditions.  suggest the use of gamma to reduce the performance gap beten the two faces under poor lighting conditions. From these results,  can provide key solutions to resolve the issues associated with texture-based approaches.
['Mi-Young Cho', 'Young-Sook Jeong']
I am writing a research paper on a idea Face recognition performance comparison between fake faces and live faces
[{"from": "human", "value": "I am writing a research paper on a idea Face recognition performance comparison between fake faces and live faces"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Face recognition is a widely used biometric technology because it is both user friendly and more convenient to use than other biometric approaches. Hover, naive face recognition systems that do not support any type of liveness detection can be easily spoofed using just a photograph of a valid user. Face liveness detection is a key issue in the field of security systems that use a camera. Unfortunately, it is not easy to detect face liveness using existing methods, assuming that there are print failures and overall image blur. With the development of display devices and image capturing technology, it is possible to reproduce face images similar to real faces. Therefore, the number of attacks using a photograph or video displayed on a screen rather than paper will increase. In this study,  compare test results using live faces and high-definition face videos from light-emitting diode (LED) display devices and analyze the changes in face recognition performance according to the lighting direction. Experimental results show that there is no significant difference beten live faces and not live faces under good lighting conditions.  suggest the use of gamma to reduce the performance gap beten the two faces under poor lighting conditions. From these results,  can provide key solutions to resolve the issues associated with texture-based approaches."}]