GSoC/2017/StatusReports/YingjieLiu: Difference between revisions
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Face recognition in digiKam is now implemented using Local Binary Patterns Histograms(LBPH) from OpenCV. However, since the performance is not as expected, the algorithm needs to be improved. Another two face recognition algorithms named Eigenfaces and Fisherfaces are partially implemented in the face engine and the two algorithms will be finalized. One sufficient reason that recognition cannot work is that we don’t have any code for pose estimation and normalization. To improve face recognition, new face algorithm with pose estimation and normalization will be added. The algorithms will be selectable in GUI by users. Besides the algorithms, the face region in database should be synchronized when the image is transformed. | Face recognition in digiKam is now implemented using Local Binary Patterns Histograms(LBPH) from OpenCV. However, since the performance is not as expected, the algorithm needs to be improved. Another two face recognition algorithms named Eigenfaces and Fisherfaces are partially implemented in the face engine and the two algorithms will be finalized. One sufficient reason that recognition cannot work is that we don’t have any code for pose estimation and normalization. To improve face recognition, new face algorithm with pose estimation and normalization will be added. The algorithms will be selectable in GUI by users. Besides the algorithms, the face region in database should be synchronized when the image is transformed. | ||
== Proposal == | |||
[https://drive.google.com/open?id=0B6jcOoU2JXrpZVlZZzVyc2VTdFE] | |||
== Work report == | == Work report == | ||
'''Community Bonding: Eigenfaces Algorithm Finished For DigiKam''' | |||
In my work I have added Eigenfaces module in digiKam for face recognition enhencement. My work including 4 parts: | |||
(1) UI modification. | |||
(2) Algorithm selection. | |||
(3) Database modification. | |||
(4) Eigenfaces algorithm. | |||
The details and screencast can be seen in my blog post [https://yjwudi.github.io/2017/06/01/community-bonding/] | |||
==Blog Post== | |||
[https://yjwudi.github.io/2017/06/01/community-bonding/] | |||
Revision as of 06:49, 1 June 2017
digiKam: Face Management Improvements
Face recognition in digiKam is now implemented using Local Binary Patterns Histograms(LBPH) from OpenCV. However, since the performance is not as expected, the algorithm needs to be improved. Another two face recognition algorithms named Eigenfaces and Fisherfaces are partially implemented in the face engine and the two algorithms will be finalized. One sufficient reason that recognition cannot work is that we don’t have any code for pose estimation and normalization. To improve face recognition, new face algorithm with pose estimation and normalization will be added. The algorithms will be selectable in GUI by users. Besides the algorithms, the face region in database should be synchronized when the image is transformed.
Proposal
Work report
Community Bonding: Eigenfaces Algorithm Finished For DigiKam In my work I have added Eigenfaces module in digiKam for face recognition enhencement. My work including 4 parts: (1) UI modification. (2) Algorithm selection. (3) Database modification. (4) Eigenfaces algorithm. The details and screencast can be seen in my blog post [2]