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 | 1 | initial version |
This is a not an OpenCV question, so it doesn't really belong here. Don't take this personal: If you have problems with Open Source projects, then use the bug tracker of the specific project. Most github projects have a bug tracker, so does this one:
You have luck, since I am browsing here regularly, but this is definitely not the case for other project authors. Regarding your question. Without knowing anything about setup and data, it's nearly impossible to recreate problems. The program you have found was a sample application I hacked up in some minutes, just to show case how to connect the Python framework with OpenCV. I didn't invest much time in checking wether the input data is valid or not.
The specific problem you see is located in the Nearest Neighbor classifier:
I am short on time and I can't support this project a lot. So if anyone finds a bug, he could make a Pull Request and I'll put it upstream.
Algorithmically. You are asking for a very tough problem, but you seem to expect a simple answer. There isn't one. Do you have a lot of difference in illumination? Then a Tan Triggs preprocessing might help. Are the objects in your image rotated? Then do a geometric normalization on your input images, this will enhance recognition rates. The face recognition algorithms I have contributed are neither rotation-invariant, nor scale-invariant. If I had one of those I would go out and make a million of dollars.
| | 2 | No.2 Revision |
This is a not an OpenCV question, so it doesn't really belong here. Don't take this personal: If you have problems with Open Source projects, then use the bug tracker of the specific project. Most github projects have a bug tracker, so does this one:
You have luck, since I am browsing here regularly, but this is definitely not the case for other project authors. Regarding your question. Without knowing anything about setup and data, it's nearly impossible to recreate problems. The program you have found was a sample application I hacked up in some minutes, just to show case how to connect the Python framework with OpenCV. I didn't invest much time in checking wether the input data is valid or not.
The specific problem you see is located in the Nearest Neighbor classifier:
I am short on time and I can't support this project a lot. So if anyone finds a bug, he could make a Pull Request and I'll put it upstream.
Algorithmically. You are asking for a very tough problem, but you seem to expect a simple answer. There isn't one. Do you have only one image per person? Then Local Binary Pattern Histograms might help, at least a lot of publications report it. Do you have a lot of difference in illumination? Then a Tan Triggs preprocessing might help. Are the objects in your image rotated? Then do a geometric normalization on your input images, this will enhance recognition rates. The face recognition algorithms I have contributed are neither rotation-invariant, nor scale-invariant. If I had one of those I would go out and make a million of dollars.
| | 3 | No.3 Revision |
This is a not an OpenCV question, so it doesn't really belong here. Don't take this personal: If you have problems with Open Source projects, then use the bug tracker of the specific project. Most github projects have a bug tracker, so does this one:
You have luck, since I am browsing here regularly, but this is definitely not the case for other project authors. Regarding your question. Without knowing anything about setup and data, it's nearly impossible to recreate problems. The program you have found was a sample application I hacked up in some minutes, just to show case how to connect the Python framework with OpenCV. I didn't invest much time in checking wether the input data is valid or not.
The specific problem you see is located in the Nearest Neighbor classifier:
I am short on time and I can't support this project a lot. So if anyone finds and fixes a bug, he could please make a Pull Request and I'll put it upstream.
Algorithmically. You are asking for a very tough problem, but you seem to expect a simple answer. There isn't one. Do you have only one image per person? Then Local Binary Pattern Histograms might help, at least a lot of publications report it. Do you have a lot of difference in illumination? Then a Tan Triggs preprocessing might help. Are the objects in your image rotated? Then do a geometric normalization on your input images, this will enhance recognition rates. The face recognition algorithms I have contributed are neither rotation-invariant, nor scale-invariant. If I had one of those I would go out and make a million of dollars.
