**5.1 Experimental results using the ORL face image database**

ORL face image database consist of 40 persons, 36 of them are men and the other 4 are women. Each of them has 10 poses. The poses were taken at different time with various kinds of lighting and expressions (eyes open/close, smiling/not smiling) [Research Center of Att, 2007]. The face position is frontal with 10 up to 20% angles. The face image size is 92x112 pixels as shown in Figure 2.

Fig. 2. Face Images of ORL Database

The experiments are employed for 5 times, and for each experiment 5, 6, 7, 8 and 9 poses for each person are used. The rest of training set, i.e. 5, 4, 3, 2 and 1, will be used as the testing [Arif et al., 2008b] as seen in Table 1

The Maximum Non-Linear Feature Selection of Kernel Based on Object Appearance 153

Fig. 4. Experimental Results on ORL Face Image Database Using 6 Training Set

recognition rate decreased to be 99.67% as seen in Figure 5 [Arif et al., 2008b].

Fig. 5. Experimental Results on ORL Face Image Database Using 7 Training Set

more than 24 dimensions [Arif et al., 2008b].

Figure 6 is the experimental results of the 4th scenario. In this scenario, 8 training sets for each person were used, whereas the number of dimensions used was 320. Figure 6 shows that the recognition rate tended to increase significantly for experimental results using less than 23 dimensions, whereas 100% recognition rate occurred for experimental results using

In the last scenario, 9 training sets were used, whereas the number of dimension used was 360, as seen in Figure 7. Similarly to the previous scenario, the recognition rate tended to increase when experimental used less than 6 dimensions, while using 7 dimensions resulted

In the 3rd scenario, training set used for each person was 7, whereas the number of dimensions used was 280. The more number of training set used, the number of dimension is increased. In this scenario, the maximum recognition rate was 100%, it occurred when 23 until 53 dimensions were used, whereas when more than 53 dimensions were used,


Table 1. The Scenario of ORL Face Database Experiment

In this experiment, each scenario used different dimension. The 1st, 2nd, 3rd, 4th, and 5th scenarios used 200, 240, 280, 320, and 360 dimensions respectively. The result of the 1st scenario can be seen on the Figure 3 [Arif et al., 2008b]

Fig. 3. Experimental Results on ORL Face Image Database Using 5 Training Set

Figure 3 shows that the more number of dimensional used, the higher recognition rate, but the recognition decreased on the certain dimension. As seen in Figure 3, recognition rate decreased into 95% when 200 dimensions were used. The first maximum recognition rate, which was 97.5%, occurred when 49 dimensions were used in this experiment [Arif et al., 2008b].

In the 2nd scenario, the maximum dimension used was 240 (240=40\*6) training set. The first maximum recognition rate occurred when 46 dimensions were used, this was 99.375%. When 1 until 46 dimensions were used, recognition rate increased proportionally to the number of dimension used, but when 47 until the 240 dimensions were used, the recognition rate tended to be stable, with insignificant fluctuations as seen in Figure 4 [Arif et al., 2008b].

152 Principal Component Analysis

1st 5 5 200 200 2nd 6 4 240 160 3rd 7 3 280 120 4th 8 2 320 80 5th 9 1 360 40

In this experiment, each scenario used different dimension. The 1st, 2nd, 3rd, 4th, and 5th scenarios used 200, 240, 280, 320, and 360 dimensions respectively. The result of the 1st

Fig. 3. Experimental Results on ORL Face Image Database Using 5 Training Set

Figure 3 shows that the more number of dimensional used, the higher recognition rate, but the recognition decreased on the certain dimension. As seen in Figure 3, recognition rate decreased into 95% when 200 dimensions were used. The first maximum recognition rate, which was 97.5%, occurred when 49 dimensions were used in this experiment [Arif et

In the 2nd scenario, the maximum dimension used was 240 (240=40\*6) training set. The first maximum recognition rate occurred when 46 dimensions were used, this was 99.375%. When 1 until 46 dimensions were used, recognition rate increased proportionally to the number of dimension used, but when 47 until the 240 dimensions were used, the recognition rate tended to be stable, with insignificant fluctuations as seen in Figure 4 [Arif

For Each Person Total

Training Testing Training Testing

Scenario Data Quantity

Table 1. The Scenario of ORL Face Database Experiment

scenario can be seen on the Figure 3 [Arif et al., 2008b]

al., 2008b].

et al., 2008b].

Fig. 4. Experimental Results on ORL Face Image Database Using 6 Training Set

In the 3rd scenario, training set used for each person was 7, whereas the number of dimensions used was 280. The more number of training set used, the number of dimension is increased. In this scenario, the maximum recognition rate was 100%, it occurred when 23 until 53 dimensions were used, whereas when more than 53 dimensions were used, recognition rate decreased to be 99.67% as seen in Figure 5 [Arif et al., 2008b].

Fig. 5. Experimental Results on ORL Face Image Database Using 7 Training Set

Figure 6 is the experimental results of the 4th scenario. In this scenario, 8 training sets for each person were used, whereas the number of dimensions used was 320. Figure 6 shows that the recognition rate tended to increase significantly for experimental results using less than 23 dimensions, whereas 100% recognition rate occurred for experimental results using more than 24 dimensions [Arif et al., 2008b].

In the last scenario, 9 training sets were used, whereas the number of dimension used was 360, as seen in Figure 7. Similarly to the previous scenario, the recognition rate tended to increase when experimental used less than 6 dimensions, while using 7 dimensions resulted

The Maximum Non-Linear Feature Selection of Kernel Based on Object Appearance 155

and center lighting), various expressions (normal, smiling, sad, sleepy, surprising, and wink) and accessories (wearing or not wearing glasses) [Yale Center for Computational

1st 5 97.5 49 2nd 6 99.375 46 3rd 7 100 23 4th 8 100 24 5th 9 100 7

Table 2. The ORL Face Database Recognition Rate using Maximum Feature Value Selection

The experiments were conducted for 6 scenarios, for each scenario, 5, 6, 7, 8, 9, and 10 training set were used. The rest of each data sample for every experiment, i.e. 6, 5, 4, 3, 2 and

1st 5 6 75 90 2nd 6 5 90 75 3rd 7 4 105 60 4th 8 3 120 45 5th 9 2 135 30 6th 10 1 150 15

Data Quantity For Each Person Total Training Testing Training Testing

The First Maximum

Recognition Rate Dimension

Vision and Control, 2007] as shown in Figure 8.

Method of Nonlinear Function based on KPCA

Fig. 8. Face Sample of Images of YALE Database

Scenario

1, were used as testing set as listed in Table 3 [Arif et al., 2008b].

Table 3. The Scenario of the YALE Face Database Experiment

Sample for Each Person

Scenario Number of Training

in 100% recognition rate, using 8 dimension resulted in 97% recognition rate, and 100% recognition rate was yielded from experimental results using more than 9 dimensions, as shown in Figure 7 [Arif et al., 2008b].

Fig. 6. Experimental Results on ORL Face Image Database Using 8 Training Set

Fig. 7. Experimental Results on ORL Face Image Database Using 9 Training Set

The maximum recognition rate for all scenarios can be seen in Table 2. This table shows that the more number of training set used, the higher recognition rate achieved, whereas the first maximum recognition rate tended to occur on the lower dimension inversely proportional to the number of dimensions used [Arif et al., 2008b].
