Combining Global and Local Classifiers for Lipreading
نویسندگان
چکیده
This paper presents a novel method of combining global and local classifiers to form a more powerful classifier for lipreading. The global classifier uses Discrete Fourier Transform (DFT) to derive an image representation that captures global information. The local classifier uses block-based Gabor Wavelets Transform (BGWT) to extract local information of the input image. These two classifiers are then combined to form the final classifier. The proposed method is evaluated on Bimodal Chinese Audio-Video Database (HIT Bi-CAVDB). Experimental results show that the global and local features can complement each other. The recognition rate of combined classifier is superior to each of the individual classifiers and increase about 5% up to 82.45%.
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