Face recognition using decimated redundant discrete wavelet transforms |
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Authors: | Deqiang Li Xusheng Tang Witold Pedrycz |
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Affiliation: | 1. Shenyang Institute of Automation, Chinese Academy of Sciences, Key Laboratory of Opto-Electronic Information Processing, CAS. Key Laboratory of Image Understanding and Computer Vision, Shenyang, 110015, China 2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, T6G 2V4, Canada 3. Systems Research Institute, Polish Academy of Sciences, 01-447, Warsaw, Poland
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Abstract: | As discrete wavelet transform (DWT) is sensitive to the translation/shift of input signals, its effectiveness could be lessened
for face recognition, particularly when the face images are translated. To alleviate drawbacks resulted from this translation
effect, we propose a decimated redundant DWT (DRDWT)-based face recognition method, where the decimation-based DWTs are performed
on the original signal and its 1-stepshift, respectively. Even though the DRDWT realizes the decimation, it enables us to
explore the translation invariant DWT representation for the periodic shifts of the probe image that is the most similar to
the gallery images. Therefore, it can solve the problem of translation sensitivity of the original DWT and address the translation
effect occurring between the probe image and the gallery image. To further improve the recognition performance, we combine
the global wavelet features obtained from the entire face and the local wavelet features obtained from face patches to represent
both holistic and detail facial features, apply separate classifiers to global and local features and combine the resulted
global and local classifiers to form an ensemble classifier. Experimental results reported for the FERET and FRGCv2.0 databases
show the effectiveness of the DRDWT method and quantify its performance. |
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