A Color Channel Fusion Approach for Face Recognition
Due to high dimensionality of images or generated color features, different color channels are usually processed separately and then concatenated together into a feature vector for classification. This makes channel fusion a crucial step in color FR systems. However, existing methods simply conc...
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| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/81594 http://hdl.handle.net/10220/39558 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Due to high dimensionality of images or generated
color features, different color channels are usually processed
separately and then concatenated together into a feature vector
for classification. This makes channel fusion a crucial step in
color FR systems. However, existing methods simply concatenate
channel-wise color features without identifying the importance
or reliability of features in different color channels. In this
paper, we propose a color channel fusion (CCF) approach using
jointly dimension reduction algorithms to select more features
from reliable and discriminative channels. Experiments using two
different dimension reduction approaches, two different types of
features on 3 image datasets show that CCF achieves consistently
better performance than color channel concatenation (CCC)
method which deals with different color channels equally. |
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