CHAPTER 6 RESULTS AND DISCUSSIONS

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1 151 CHAPTER 6 RESULTS AND DISCUSSIONS In this chapter the performance of the personal identification system on the PolyU database is presented. The database for both Palmprint and Finger Knuckle Print is available at edu.hk/~biometrics/.the samples available in the database are divided into two sets, namely the training set and the testing set. The training set consists of four samples from each of 150 persons and similarly the testing test consists of 8 samples from each of 150 persons. The evaluation of the system is done based on the error rates and recognition rate. 6.1 PERFORMANCE EVALUATION METRICS False Acceptance Rate (FAR): This is defined as a percentage of impostors accepted by the biometric system. In identification biometric system the users are not making claims about their identity. Hence it is necessary that this percentage is as small as possible so that the person not enrolled in the system must not be accepted by the system. Thus False Acceptance must be minimized in comparison to False Rejections. False Rejection Rate (FRR): This is defined as a percentage of genuine users rejected by the biometric system. In verification biometric system the user will make claims of their identity and hence the system must not reject an enrolled user and number of False Rejections must be kept as small as possible. Thus False Rejection must be minimized in comparison to False Acceptance.

2 152 Genuine Acceptance Rate (GAR): This is defined as a percentage of genuine users accepted by the system. It is given by GAR=100-FRR. Equal Error Rate (EER): This is defined as the point of intersection on the graph on which both FAR and FRR curves are plotted. Receiver Operating Characteristics (ROC): It is a plot of Genuine Acceptance Rate against False Acceptance Rate. 6.2 PALMPRINT RECOGNITION RESULTS In Palmprint Recognition, the images in the database are first enhanced using Median filter and a Region Of Interest (ROI) of size is cropped for feature extraction. The training and testing phase are considered for three cases. Case (i) In the first case, the ROI is filtered using circular Gabor filter.the LGXP feature for a single orientation is then obtained as explained in the section 3.3. Euclidean distance is used for matching.the threshold is initially set to zero and the matching score values are obtained. It is then increased in steps of 0.1 and for each of these trails FAR, FRR and GAR are computed. The values obtained are shown in Table 3.1. The graph is then plotted for FRR, FAR against Threshold and the EER is determined which is point of intersection of the two curves. For each ROI of and sub-image of 3 3, the feature vector consists of 1600 elements each being one byte wide. Case (ii) In the next phase, six orientations are used and the Multiple Orientation LGXP (MOLGXP) feature is obtained and the parameters are

3 153 computed during the testing phase where the size of the feature vector in this case is increased to 9600 elements of one byte each. Case (iii) In the third phase PCA feature is also computed along with MOLGXP feature. Next the matching scores from individual matchers are obtained and fusion of these scores is done as explained in section 3.5 for matching purpose. The performance measure of the LGXP, MOLGXP, MOLGXP + PCA is given in Table 6.1 below. As per above discussion and the results shown in section 3.6, the error rates are selected at the threshold for which FAR is minimized. The corresponding FRR, FAR, GAR and EER values is shown in Table 6.1. Table 6.1 Error rates and Genuine Acceptance rate for Palmprint Recognition system Recognition Method FRR% FAR% EER % GAR% LGXP MOLGXP MOLGXP + PCA The graphical comparison for the results shown in Table 6.1 is shown in Figure 6.1, 6.2, 6.3 and 6.4 respectively.

4 154 Figure 6.1 Comparison of FRR for Palmprint Recognition system From the above graph it is observed that the False Rejection Rate is 5.86% for LGXP feature where only single orientation is considered and is the highest when compared to other techniques. The Palmprint is rich in line like structures which are oriented in different directions and hence multiple orientations has been considered for feature extraction and MOLGXP features are obtained in the next phase and it is found that FRR is decreased to 3.5%. Thus it is observed that more the information from Palmprint is coded as features, the feature vector size not only increases but also reduces the error. For the MOLGXP + PCA feature the FRR further decreases to 1.02%. Thus it found that a feature fusion at score level using Sum rule provided better results and also as the feature vector size has increased, more users can be accommodated into the system. The Figure 6.2 shows the comparison of FAR for the proposed Palmprint Recognition methods. It is highest for LGXP feature and lowest for MOLGXP + PCA fusion. It is equal to and it is reduced to for MOLGXP. For the fusion scheme it is reduced to the

5 155 Figure 6.2 Comparison of FAR for Palmprint Recognition system The Figure 6.3 shows the comparison of EER for the Palmprint Recognition. It is around 1.14% for LGXP and reduced to 0.19% and 0.13% for MOLGXP and fusion scheme respectively. Figure 6.3 Comparison of EER for Palmprint Recognition system The Figure 6.4 shows the comparison of Genuine Acceptance Rate for the Palmprint recognition. It is the highest for MOLGXP + PCA fusion scheme and is equal to 98.98%.

6 156 Figure 6.4 Comparison of GAR for Palmprint Recognition system The Receiver operating characteristics which is a plot of GAR against FAR is shown in Figure 6.5. It is observed that the curve for LGXP rises from 94% and reaches to 100%. The response is improved for MOLGXP and for the fusion scheme it is almost a constant curve at the highest value. In this work, on the Palmprint Recognition system based on LGXP and principal components features, it is observed that very low error rates and high genuine acceptance rate is obtained. The performance metrics are found to improve, as more useful information is extracted from the Palmprint and stored as template. Also fusion of features is found to provide promising results. The performance of the Palmprint Recognition System is compared with the method proposed by Zhang et al (2012) in terms of EER and it is found that the proposed method has less EER% as shown in Table 6.2.

7 157 Figure 6.5 ROC characteristics-gar Vs FAR for Palmprint Recognition system Table 6.2 Equal Error Rate for Proposed and Existing Palmprint Recognition Method Recognition Method EER% MOLGXP + PCA (proposed method) 0.13 Comp code+ Fisherpalms (Existing method) FINGER KNUCKLE PRINT RECOGNITION RESULTS In this section the algorithms such as SIFT, SURF, EMD, BEMD are used to extract the features from FKP. Then fusion of both SURF and EMD and also SURF and BEMD at score level using sum of minimum distance is carried out. The False Rejection Rate (FRR), False Acceptance Rate (FAR), Equal Error Rate (EER) and Genuine Acceptance Rate (GAR) are shown for the above algorithms in the Table 6.3.

8 158 The graphical representation for the same is shown in Figure 6.6, 6.7, 6.8 and 6.9 respectively. Table 6.3 Error rates and Recognition rate for FKP Recognition system Recognition Method FRR% FAR% EER% GAR% SIFT SURF EMD BEMD SURF + EMD SURF + BEMD Figure 6.6 Comparison of FRR for FKP Recognition system

9 159 Figure 6.7 Comparison of FAR for FKP Recognition system Figure 6.8 Comparison of EER for FKP Recognition system Figure 6.9 Comparison of GAR for FKP Recognition system

10 160 It is observed that fusion of SURF and EMD and also fusion of SURF and BEMD matching scores using sum rule of the Finger Knuckle Print provides better performance when compared to other methods without using fusion scheme. The error rates are small and also the False Rejection Rate is low at lower False Acceptance Rate. The Equal Error Rate is the smallest and is equal to 0.18% for SURF + EMD and 0.17% for SURF + BEMD. It is also noted that the Genuine Acceptance Rate is highest for the fusion schemes and is equal to 98.04% for SURF + EMD and 98.46% for SURF + BEMD. The ROC characteristic which is a plot of FAR against GAR is shown in Figure 6.10 for the Finger Knuckle Print Recognition System. Figure 6.10 ROC characteristics-gar Vs FAR for FKP Recognition system In this work, on the FKP Recognition system it is observed that very low error rates and high genuine acceptance rate is obtained for the fusion scheme. The performance of the FKP Recognition System is compared with the method proposed by Zhang et al (2010) in terms of EER and it is found that the proposed method has less EER% as shown in Table 6.4.

11 161 Table 6.4 Equal Error Rate for the Proposed and Existing FKP Recognition Method Recognition Method EER% SURF + EMD (proposed method) 0.18 SURF + BEMD (proposed method) 0.17 LGIC Technique (Existing method) MULTIMODAL RECOGNITION RESULTS This section presents the performance metrics of the proposed Multimodal Recognition system. The Error Rates and the Genuine Acceptance Rate of the multimodal system are shown to be the best for fusion scheme using Weighted Sum Rule with optimized weights and the results as obtained in section 5.4 is shown in Table 6.5. Table 6.5 Error rates and Recognition rate for Multimodal system Recognition Method FRR% FAR% EER% GAR% MOLGXP+ PCA +SURF +EMD (Proposed Method -1) MOLGXP+ PCA +SURF +BEMD(Proposed Method -2) In the table, it is observed that the error rates are lower and recognition accuracy is higher for the Proposed Method -2 in comparison to the Proposed Method -1.Though this is true, it is also evident that BEMD is a slow algorithm and hence takes more time for feature extraction. It is also observed that the difference in error rates and recognition accuracy between the two combinations is very low. Thus it is concluded that fusion of

12 162 Palmprint feature (MOLGXP and PCA) and Finger Knuckle Print feature (SURF and EMD) is fast and also provides a very good performance and is suitable multimodal biometric system which can be implemented with less error and high accuracy. The performance is compared with the technique proposed by Meraoumia1et al (2011) where the author has used 1D Log Gabor filter to extract features from both palmprint and finger knuckle print images and the proposed technique is found to perform better in terms of error rates and recognition rate. The Table 6.6 below shows the results for existing and proposed method in terms of EER and the graphical representation is shown in Figure Table 6.6 Equal Error Rate for Existing and Proposed Multimodal Recognition Method Recognition Method EER % Existing Method(Log Gabor Filter-real and imaginary- Min rule) Proposed Method -1(MOLGXP+PCA,SURF+EMD-Min rule) Proposed Method -2(MOLGXP+PCA,SURF+BEMD- Min rule) Proposed Method -1(MOLGXP+PCA,SURF+EMD-Weighted Sum rule (OW)) Proposed Method -2(MOLGXP+PCA,SURF+BEMD- Weighted Sum rule (OW))

13 163 Figure 6.11 Comparison of EER for Existing and Proposed Method 6.5 SUMMARY In this chapter, the performance of the unimodal and multimodal system is presented. In the unimodal system using Palmprint recognition it is observed that as more information is extracted, better the performance of the biometric system. Secondly it is found that the fusion scheme further improves the results with an EER of 0.13% and highest recognition rate of 98.98% is achieved. Next in the unimodal system features are extracted from the Finger Knuckle Print using four algorithms and the results show that features extracted using SURF, EMD and BEMD provides better performance in comparison to SIFT algorithm. Next the matching scores both from SURF and EMD matchers, SURF and BEMD matchers are fused using sum rule. An EER of 0.18% and GAR of 98.04% for SURF + EMD and EER of 0.19% and GAR of 98.46% for SURF + BEMD is achieved.

14 164 Next results of multimodal fusion of Palmprint and Finger Knuckle Print at matching score level using weighted Sum rule (OW) is presented. When comparing the results of unimodal system with that of the multimodal system we find that multimodal system provides a very low FAR, FRR, ERR and high GAR. Finally from the experimental results it is concluded that Palmprint feature (MOLGXP and PCA) and Finger Knuckle Print feature (SURF and EMD) provides a very good performance with EER of 0.003% and GAR of 99.92% and is considered as a suitable multimodal biometric system which can be implemented with less error and high accuracy. The proposed multimodal system also provides better performance than that proposed by Meraoumial et al (2011).