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We propose a face recognition method based on halftone binary image using SVM classifier. In this method, a training set and a testing set of halftone images are created from a face database of gray images. Then, features are extracted from halftone images and a multi-class SVM model is created. To extract features from a halftone image, the image...
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Context 1
... us see some of the landmark points considered or generated from a face image and see whether assembly of landmark points retains any cue for the image from which they are extracted. Figure-1(a) shows the face with mesh graph on it and Figure-1(b) shows only the graph. It is clearly seen that there is no visible cue of the face in Fig.-1b. ...
Context 2
... us see some of the landmark points considered or generated from a face image and see whether assembly of landmark points retains any cue for the image from which they are extracted. Figure-1(a) shows the face with mesh graph on it and Figure-1(b) shows only the graph. It is clearly seen that there is no visible cue of the face in Fig.-1b. ...
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Citations
... The holistic approach is of special interest as the features considered fully represent the data. A slightly different approach is the halftone based face recognition [18] in which human recognizable features are extracted which is much reduced in size yet contains all recognizable features of a face which is then used for recognition. The approach gave considerable higher recognition rate as compared to feature-based approach. ...
... In [18], three different ways of generating HRF from a halftone image are described. The method is simple, from a gray scale image, generate a halftone image and then from the halftone image, generate the HRF image as shown in Figure- Figure 3(b), then HRF feature from halftone image is shown in Figure-3(c). ...
... Note that the image is not in binary but a gray scale image generated from the halftone binary image. In this paper, we consider mainly the Row Column Average features to generate HRF as this features gives high recognition rate as reported in [18]. ...
Face recognition is done gray or color images. Face recognition can be also done in binary images. Halftone images are binary images having gray shade information. These images are more informative as compared to binary images. Halftone images can be generated from different kernels. The quality of a halftone image depends on the kernel used to generate it. Halftone images can be used to generate human recognizable features for face recognition. The human recognizable features are fed to multiclass SVM classifiers for face recognition. The performance of using different human recognizable features generated from halftone images of different standard kernels is studied. It is found that some of kernels give higher recognition rate than the other. Also, the performance is also dependent on size of the window used for feature extraction. In most cases, the minimum size window 3 × 3 gives higher recognition rate in most of the cases. Of the standard kernels Frankie Siera-3, Stucki kernels give higher recognition rate and of the proposed kernels P2 kernel gives higher recognition rate than other proposed kernels.
