Appearance-based Binary Age Classification in .NET

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Appearance-based Binary Age Classification
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4.2 The Effect of Preprocessing and Image Resolution
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Preprocessing algorithms were designed to get rid of any variations in the lighting conditions in the images. The two preprocessing methods tested were histogram equalization and histogram equalization followed by brightness gradient removal. Brightness gradient removal cannot be used as a stand-alone preprocessing step and so was used in conjunction with histogram equalization to check if it has any significant impact on the classifier accuracies. Hence, the data set available was preprocessed to get three sets unprocessed images, histogram equalized images and images processed through histogram equalization with brightness gradient removal. In order to find out the optimum image resolution to be used for classification, the available image set was downsampled to obtain 25 25 and 20 20 size images, along with the existing image resolution of 29 29. The reason behind trying out lower image resolutions was that by downsampling, certain high-frequency peculiarities specific to people, such as moles, etc., would be lost, leading to better classification accuracies. Figure 13.4 shows the plot of classification accuracy at various image resolutions under different image preprocessing conditions. The result in Figure 13.4 represents the model obtained using male examples only. It may be noted that similar results were obtained for models trained using females, as well as for models obtained by using combined male and female examples. From the plot it can be seen that there is an improvement in the classification accuracy of about 4 5 % for an increase in the facial image resolution from 20 20 to 29 29. This improvement can be seen for all image preprocessing conditions.
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4.3 The Effect of Pose Variation
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In order to be effective for real-life applications, the age classifier should provide stable classification results in the presence of significant pose variations of the faces. In order to test the performance of the classifier in the presence of pose variations, the following test was conducted. The faces of 12 distinct people were saved under varying poses from frontal facial image to profile facial image. The best classifiers obtained for each resolution were used on these sets of faces to check for the consistency of classification accuracy. Thus, if, for a given track having ten faces, eight faces were classified correctly, the accuracy for that track was tabulated as 80 %. In this way, the results were tabulated for all three classifiers.
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Males 85 Unprocessed 80 Accuracy 75 70 65 60 20 20 25 25 Resolution 29 29 Brightness grad. removal + Histogram eq. Histogram equalization
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Figure 13.4 The effect of preprocessing on age classification. Age classification accuracy (male only database) at various image resolutions under different preprocessing methods.
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Empirical Analysis
120 100 Accuracy 80 60 40 20 0 20 20 25 25 Resolution 29 29
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Figure 13.5 The effect of pose. Age classification accuracy at various image resolutions for 12 sets of images with pose variations. The results for the performance of the classifier under pose variation are summarized in Figure 13.5. From this experiment it is easy to see that for all three classifiers at least 11 out of 12 sets of faces gave an accuracy of greater than or equal to 60 %. This result is significant as it points to a possibility of using the sampled classifier output for all the faces obtained in a given track to classify the person in the track. Figure 13.5 indicates that for all the resolutions, at least 11 of the 12 sets of images with pose variation gave an accuracy of greater than 50 %.
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4.4 The Effect of Lighting Conditions
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In real-life scenarios, there are changes in the lighting conditions that can produce diverse intensity gradients across the facial images. The intensity gradients could be across the entire face or only across half the face, depending on the position of the light source. To test the performance of the classifier under such circumstances, gradual intensity gradients were introduced in the available set of images and tested with the available classifiers trained from the images without the brightness gradients. The purpose of the testing was to check the effectiveness of the preprocessing steps in taking care of such gradients. Figure 13.6 indicates that, as expected, the brightness gradient removal method for preprocessing worked best in the presence of vertical and horizontal gradients in the image. However, in the presence of diagonal gradients in the image, histogram equalization gave results equally as good as the results obtained using histogram equalization with brightness gradient removal as the preprocessing step.
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