Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents an ultra-fast detection algorithm for locating faces in grey scale videos.  first use motion detection to reduce the working area and find the approximate position of the head. Then a morphology-based technique is applied in this area to detect eye-analogue and lips-analogue regions. Next, the resulting components are used to search for potential facial features. Finally  select from the candidate triplets, the one that best represents a real face, calculating a fitness which takes into account things such as the symmetry and the proximity with the extrapolated position of the face. In order to achieve the maximal speed-up,  use the vector parallelism provided by the SIMD (simple instruction multiple data) extensions, available on most mainstream processors. The final program runs 65 times faster than the real-time. Experiments demonstrate that the success rate for single face videos reaches 85% in good conditions and can go down to 60% in harder cases. This approach can be useful in many applications, where the detection rate is not as important as the computation time, such as video face identification, or human-computer visual interfaces.
['Olivier Vermeulen', 'Antoine Manzanera', 'Lionel Lacassagne']
I am writing a research paper on a idea Ultra Fast Grey Scale Face Detection Using Vector SIMD Programming
[{"from": "human", "value": "I am writing a research paper on a idea Ultra Fast Grey Scale Face Detection Using Vector SIMD Programming"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents an ultra-fast detection algorithm for locating faces in grey scale videos.  first use motion detection to reduce the working area and find the approximate position of the head. Then a morphology-based technique is applied in this area to detect eye-analogue and lips-analogue regions. Next, the resulting components are used to search for potential facial features. Finally  select from the candidate triplets, the one that best represents a real face, calculating a fitness which takes into account things such as the symmetry and the proximity with the extrapolated position of the face. In order to achieve the maximal speed-up,  use the vector parallelism provided by the SIMD (simple instruction multiple data) extensions, available on most mainstream processors. The final program runs 65 times faster than the real-time. Experiments demonstrate that the success rate for single face videos reaches 85% in good conditions and can go down to 60% in harder cases. This approach can be useful in many applications, where the detection rate is not as important as the computation time, such as video face identification, or human-computer visual interfaces."}]