Classifier Used and Feature Selection and Optimization Technique

As liveness classification is a two-class classification problem and the dimension of the feature used are not large, so various pair-wise classifier such as a Euclidean distance, city block distance, Chebyshev distance, cosine distance, spearman distance, Hamming distance and Jaccard distance which are available in the literature are exploited here. Among them, the Jaccard distance produced the best result. For feature selection first found the performance of the individual features and then the feature producing the best result is combined with the second best result and so on.

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4.2.2.3. Experimental Result Details

The liveness experimental results with various quality-based features are explained in this subsection. For training, five samples from each class from the fake and the genuine categories are used to produce the training model (the feature values are averaged to get the feature representation of each class) and the remaining five samples from each class of fake and genuine images were used for testing. The performances of the above-mentioned features individually and in combination are presented in Table 4.7.

The table illustrates the satisfactory performance of the proposed quality feature for both aspects of liveness detection (i.e. ability to classify genuine and fake samples correctly). It can be seen from the above Table that the focus-related features worked for detecting liveness. Aspect-related features worked better than the focus-related feature. Among the aspect-related features, the performance of the QF4 (i.e. the ratio of iris and pupil radius) was the best. Furthermore, the group of contrast-related features produced the best results. Among them, QF 18 i.e. the green channel contrast, outperformed the other quality features introduced here. As mentioned previously, a combination of this feature is also used to analyse the performance. Fused the features according to the performance rank so, combined the 1st rank feature with 2nd and so on. It can be concluded from Table 4.7 that the combination of the contrast-based features and the aspect-based features produced the best result.

Moreover, it will be also quite interesting to observe the correlations between the features. It can be seen from the results that the accuracy of the features is quite close. But the features are significant because when they are combined, they boost the accuracy as opposed to when they are applied individually.

Although the contrast-related features have worked exceptionally well in the proposed schema, unfortunately, such features can be attacked by intruders at the software level by using photometric normalisation. However, for such scenarios of attack, the intruder will be required to have technical details about the inner functionality and a clear architecture of the liveness system for tonal correction of the fake images. Moreover, they need to attack at the software level rather the sensor level, which is more difficult than a direct attack.

The feature distributions of the best discriminative features, such as the pupil and the iris radius QF4 and contrast quality features QF19, are shown in Figure 4.12 and the contrast quality features (QF14, QF17, and QF18) are shown in Figure 4.11. Although the cumulative frequency or the probability distributions, i.e. the score of the feature normalised at each class level, would have been a better measure, for better discernibility of the feature, this feature distribution is used. Moreover, the feature distribution is also a better measure to reflect the effectiveness of the feature at the class level and the database level. In each graph, the quality feature score distribution of the entire genuine sample is represented by a green line and the fake sample by a blue line. Along the X-axis is the

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Table 4.7: Liveness detection performance of the various individual and combinations of image quality-based features.

Feature Test averaged accuracy in % achieved for Fake samples Genuine samples

Fo cu s F ea tu res QF1 81 79 QF2 70 83 QF3 76 80 Asp ec t f ea tu res QF4 95 94 QF5 86 82 QF6 87 84 QF7 84 86 QF8 87 84 QF9 85 78 QF10 84 70 QF11 80 70 QF12 78 78 QF13 85 86 C o n tr ast m ea su res QF14 96 96 QF15 88 89 QF16 89 90 QF17 96 95 QF18 98 97 QF19 95 94 C o m b in atio n QF14+QF18 98 98 QF14+QF17+QF18 98 99 QF14+QF17+QF18+QF19 99 99 QF4+QF14+QF17+QF18+QF19 100 100

sample and along the Y-axis is the feature value. In each of the lines in the graph, the first ten feature values represent the feature value of the ten fake/genuine samples of the first

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class, the next ten from the second class and so on. It is clear from the graphs that the feature values of the fake and genuine samples have a discernible difference within each class. Whereas, if database-level feature distribution is considered, then this feature value would have a certain overlapping region. Therefore that creates confusion in the classification of ‘alive’ and fake data at the database level.

(a)

(b)

(c)

Figure 4.11. Feature distributions of the best discriminative quality features for the genuine and fake samples of each class (a) Global contrast QF14, (b) Red channel contrast QF17, and (c) Green channel contrast QF18 [275].

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(a)

(b)

Figure 4.12: Feature distributions of (a) Blue channel contrast QF18, (b) QF4 (is the ratio of the pupil and the iris radius) [275].