Liveliness Detection by Multi-angle Sequence of the Eye

The liveliness of the data will be detected by the motion of the eye. During enrolment, the user will be asked to look straight, up, left and right angle keeping the face straight towards the camera. Combinations of the sequence produced considering each of the views is stored in the training model. During enrolment, the user will be asked to perform any of the random

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sequence (left, right, straight and up, or up, right, straight and left, so on). A framework for the proposed technique is in Figure 4.1.

Figure 4.1. A framework of the liveliness system using a multi-angle sequence of an eye [266]. An experiment was carried out with the MASD in the visible spectrum to reflect the usefulness of the concepts. Here experiments were performed by using 4 images set for each angle from each class are randomly chosen and utilised for training and the remaining 4 set images for testing performance for each angle. Here segmentation was performed by C- means, enhancement by Haar filter, feature extraction by Dense LDP and classification by SVM. First, the ability of the multi-angle scheme to verify the user identity was checked. Table 4.1 shows the Equal Error Rate (EER) of the device using different angles.

Table 4.1: EER of the different angle Different Angle EER (%)

Looking up 0.9

Looking Left 1.1

Looking right 1.41 Looking straight 1.05

It can be seen that the best angle is looking up although all of them get competitive results compared to the classical strategy of looking straight. A possible reason for getting better

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EER in up angle is due to the visibility of more exposed sclera vessels pattern. The EER for the right and the left angle is less due to presence more amount of specular reflection on the sclera region as compared to other two angles. The results with forgeries are obtained supposing that a high-resolution picture is presented at the system with a wrong eye orientation. In this case, the ability of the system to reject such an input is given in Table 4.2.

Table 4.2: The results with forgeries obtained if the user is looking at a different angle for that of which the system have asked for

Training

Looking Up Left Right Straight Testing Up 0.9 20.12 17.88 19.56

Left 19.75 1.1 21.99 14.66

Right 15.99 22.03 1.41 19.18 Straight 19.55 15.01 19.08 1.05

It can be reflected from the above Table of each and every forgery angel the EER is high. Hence it can be concluded that authentication with different angles of sclera pattern highly affects the efficiency of the system. In the second phase of the experiment, one image from each angle was combined in image level and feature level, to determine the effect of the combination of multi-angle on the efficiency of the system. An example of the image label fusion is given as below. The results of the different fusion levels are reflected in Table 4.3.

Figure 4.2.Image label fusion of different angles [266]. Table 4.3. EER of the different level of fusion Different level of fusion EER (%)

Image Level 0.52

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It can be inferred from the above Table that image label fusion produces the best results and even the fusion of the different angle helps to increase the efficiency of the system. Next to experiment the effect of a sequence of the orientation when an individual is asked to look in a sequence: for instance straight, up, left and right but the individual looks in some other sequence, then what is the performance of the system.

Table 4.4. The results with forgeries obtained if the user is looking at a different sequence of angles from that which the system has asked for by image level fusion

Training by sequence of looking straight, up, left and right

Testing Sequence of orientation EER in % Straight, up, left and right 0.52 Straight, up, right and left 19.90 Straight, left, up and right 17.01 Straight, left, right and up 23.05 Straight, right, left and up 24.01 Straight, right, up and left 22.09 Right, left, up and straight 24.35 Right, left, straight and up 22.01 Right, up, left and straight 23.44 Right, up, straight and left 23.01 Right, straight, up and left 22.44 Right, straight, left and up 19.01 Up, straight, left and right 19.90 Up, straight, right and left 21.77 Up, left, straight and right 17.10 Up, left, right and straight 23.33 Up, straight, left, and right 24.44 Up, straight, right and left 21.13 Left, right, up and straight 11.90 Left, right, straight and up 23.4 Left, up, right, and straight 19.08 Left, up, straight and right, 22.01 Left, straight, up and right, 19.01 Left, straight, right and up, 23.02

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Table 4.5. The results with forgeries obtained if the user is looking at a different sequence of angles from that which the system has asked for, by feature level fusion

Training by sequence of looking straight, up, left and right

Testing Sequence of orientation EER in % Straight, up, left and right 0.75 Straight, up, right and left 19.99 Straight, left, up and right 19.09 Straight, left, right and up 23.95 Straight, right, left and up 24.81 Straight, right, up and left 22.39 Right, left, up and straight 24.05 Right, left, straight and up 22.81 Right, up, left and straight 23.94 Right, up, straight and left 23.81 Right, straight, up and left 22.49 Right, straight, left and up 19.01 Up, straight, left and right 19.99 Up, straight, right and left 21.8 Up, left, straight and right 17.90 Up, left, right and straight 23.77 Up, straight, left, and right 24.66 Up, straight, right and left 22.03 Left, right, up and straight 12.0 Left, right, straight and up 23.87 Left, up, right, and straight 19.99 Left, up, straight and right, 22.65 Left, straight, up and right, 19.88 Left, straight, right and up, 23.74

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It can be reflected from the above Table of each and every forgery angle sequence the EER is high. Hence it can be concluded that this sequence of eye movement can work as a good form for the liveliness of detection. The experiment is extended by training with image sequences from the dataset and testing them with a set of images captured by images of the eye of the same set of individuals from a mobile or portable screen (to stimulate a practical life scenario when the eye image can be scanned for spoofing in front of a sensor by high- resolution image). Examples of such images are given below in Figure 4.3.

(a) (b)

(c) (d)

Figure 4.3: Examples of images taken by displaying eye images from mobile [266].

The EER obtained in the result of the above-mentioned experiment was 10.05. This signifies the robustness of the system to spoofing by portable devices. Although a significant result is achieved in the experiments but trade-off of adaptability and liveness is not addressed. Therefore, further research is conducted which is summarised in the next section.

Table 4.6. Equal Error Rate of the different level of fusion by testing the mobile images Different level of fusion EER (%)

Image level 10.05

Feature level 10.79