Dr H.B Kekre et al. / International Journal of Engineering Science and Technology Vol. 2 (12), 2010, 7372-7379
Performance Comparison of Image Transforms for Palm Print Recognition with Fractional Coefficients of Transformed Palm Print Images Dr H.B Kekre Senior Professor, Computer Engg Dept, MPSTME, SVKMs NMIMS, Mumbai
[email protected]
Sudeep D. Thepade Associate Professor, Computer Engg Dept, MPSTME, SVKMs NMIMS, Mumbai
[email protected]
Ashish Varun Student, MPSTME, SVKMs NMIMS, Mumbai
[email protected]
Nikhil Kamat Student, MPSTME, SVKMs NMIMS, Mumbai
[email protected]
Arvind Viswanathan Student, MPSTME, SVKMs NMIMS, Mumbai
[email protected] Pratik Dhwoj Student, MPSTME, SVKMs NMIMS, Mumbai
[email protected] Abstract: The paper presents performance comparison of palm print recognition techniques based on fractional coefficients of transformed palm print image using six different transforms like Sine, Cosine, Walsh, Slant, Hartley and Haar. In transform domain, the energy of image gets accumulated towards high frequency region; this characteristic of image transforms is exploited here to reduce the feature vector size of palm print images by neglecting the low frequency coefficients in transformed palm print images. Six image transforms and 13 ways of taking fractional coefficients result into total 78 palm print identification methods. A database of 2350 palm print images is used as a test bed for performance comparison of the proposed palm print identification methods with help of false acceptance ratio (FAR) and genuine acceptance ratio (GAR). The experimental results in Sine, Walsh, Haar, Cosine transform have shown performance improvement in palm print identification using fractional coefficients of transformed images. In all Cosine transform at fractional coefficients of 0.78% gives best performance as indicated by higher GAR value. Thus the task of speeding up the process of palm print identification with better performance is achieved making it more suitable for real time applications.
1. Introduction Palm print recognition basically implements many of the matching characteristics used in fingerprint recognition making it one of the widely used biometrics. Both palm and finger biometrics make use of the information presented in a friction ridge impression. This information combines ridge flow, ridge characteristics, and ridge structure of the raised portion of the epidermis. The data represented by these friction ridge impressions allows a
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Dr H.B Kekre et al. / International Journal of Engineering Science and Technology Vol. 2 (12), 2010, 7372-7379 determination that correspond areas of friction ridge impressions. Because fingerprints and palms both last very long and have the qualities of uniqueness and permanence, they have been used for over a century as a trusted form of identification. However, palm recognition has been slower in becoming automated due to some restraints in computing capabilities and live-scan technologies.[11][3][10] Some palm recognition systems operate by scanning the entire palm, while others require the palms to be segmented into smaller areas to optimize performance. Maximizing reliability within either a fingerprint or palm print system can be greatly improved by searching smaller data sets.[1][4][15] The three main categories of palm matching techniques are:
minutiae-based matching
correlation-based matching
ridge-based matching
The algorithm used and the sensor that is implemented influence advantages and disadvantages of each approach. Minutiae-based matching, despite not taking advantage of the physical and visual features of the palm typically attains higher recognition accuracy, although it performs poorly with low quality images. Correlationbased matching is often quicker than minutiae based matching but is less tolerant to elastic and translational variances and noise within the image. A couple of ridge-based matching characteristics are unstable or a highresolution sensor is a prerequisite to obtain quality images. It should also be noted that the distinctiveness of the ridge-based characteristics is significantly lower than the minutiae characteristics. [15][12] Computational complexity is the major drawback as far as the existing methods of palm print identification are concerned making it very difficult to be implemented in real time applications. The paper extends the concept of using fractional coefficients of transformed images as feature vectors to palm print identification using six transforms namely Sine, Cosine, Walsh, Slant, Hartley and Haar. The performance of biometric identification techniques is generally measured using false acceptance ratio (FAR) and genuine acceptance ratio (GAR) which are elaborated in sections 1.1 and 1.2.[2][14]
1.1. Genuine Acceptance Ratio (GAR) Depending on the choice of threshold, the legitimate users’ palm prints that are genuinely accepted by the system can be from none to all images. A legitimate user is an individual that is verified against the given database. The threshold of the genuinely accepted data divided by the number of all legitimate user data is called Genuine Acceptance Ratio (GAR). Its value is one, then all legitimate users are accepted, and zero if no legitimate user is accepted.[11] 1.2. False Acceptance Ratio (FAR) Depending on the choice of threshold, the impostor’s palm prints that are falsely accepted by the system can be from none to all images. Impostor is an individual that is verified against a different stored database. The threshold of the falsely accepted data divided by the number of all impostor data is called False Acceptance Ratio (FAR). Its value is one, if all impostor data are falsely accepted, and zero if none of the impostor data is accepted.[12]
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Dr H.B Kekre et al. / International Journal of Engineering Science and Technology Vol. 2 (12), 2010, 7372-7379 2. Proposed palm print identification techniques
Fig 1. The Technique Of Fractional Coefficients
It can be observed from figure-1 that the higher frequency coefficients of transformed image can be selected to form thirteen feature vector sets as 100%, 50%, 25%, 12.5%, 6.25%, 3.125%, 1.5625%, 0.7813%, 0.39%, 0.195%, 0.097%, 0.048% and 0.020% of total number of coefficients in transformed image. When we use a specific image transform with these feature vector selections, various forms of palm print identification methods are obtained. The six transforms used here are Sine Transform, Walsh Transform, Slant Transform, Haar Transform, Cosine Transform and Hartley Transform. Six image transforms and thirteen feature vector selection methods result into total 78 palm print identification techniques discussed and analyzed in the paper.[6][7] 3. Implementation The proposed palm print identification methods have been implemented with the help of MATLAB 7.0 using a computer with Intel Core 2 Duo Processor T8100 (2.1GHz) and 2 GB RAM. The techniques are tested on palm print image database of 2350 images of 235 individuals (1175 left palm prints and 1175 right palm prints). Per individual, database has ten palm print images, five samples of each left and right palm prints. To test the performance of proposed palm print identification techniques, total 1175 queries are fired on each the left and the right palm print databases to compute FAR and GAR values per query. Finally the average FAR and average GAR values of all queries are computed and these results taking into consideration the individuals (left or right) palm prints are compared with consideration of both left and right together as a query. The similarity measure which is used for matching of query with database images used here is Mean Square Error (MSE).[5] 4. Results and Discussions 4.1 Sine Transform Figure-2 shows the average genuine acceptance rate values (GAR) of proposed palm print identification techniques implemented using Sine transform. The GAR values of considering only left palm print (DST L), only right palm print (DST R) and both (DST L_R) are shown in figure 2 with average (AVG) of all these 3 GARs. The figure shows improvement in GAR values with reducing amounts of fractional coefficients up to 0.78%, indicating that fractional coefficients give better performance than all transformed coefficient data in Sine transform. Furthermore the ruggedness of the system is enhanced when both left and right palm prints are considered. In all 0.78% fractional coefficients of Sine transformed palm print recognition proves to be better.
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Dr H.B Kekre et al. / International Journal of Engineering Science and Technology Vol. 2 (12), 2010, 7372-7379 Even the speed of palm print recognition improves with decreasing amount of fractional coefficients considered as feature vectors because of less number of comparisons needed in matching the query features with database image features resulting into faster palm print identification. DST L
DST R
DST L_R
AVG
0.8 0.75 0.7 GAR
0.65 0.6 0.55 0.5 0.45 0.4 0.02%
0.05%
0.10%
0.20%
0.39%
0.78%
1.56%
3.13%
6.25%
12.50%
25%
50%
100%
Percentage of co‐efficient
Fig 2 – Average Genuine Acceptance Rate of proposed palm print identification methods using Sine transform
4.2 Cosine Transform The average genuine acceptance rate values (GAR) of proposed palm print identification techniques implemented using Cosine transform is plotted in figure-3. The figure shows the GAR values of considering only left palm print (DCT L), only right palm print (DCT R) and both (DCT L_R) with average (AVG) of all these 3 GARs. The figure shows a gradual degradation in GAR values with reducing amounts of fractional coefficients. Further it can be observed that considering both left and right palm prints together increases the ruggedness of the system in all fractional coefficients as shown by higher GARs. 0.78% of fractional coefficients of Cosine transformed palm print recognition proves to be the best in terms of performance. [8][9]
GAR
DCT L
DCT R
DCT L_R
AVG
0.775 0.725 0.675 0.625 0.575 0.525 0.475 0.425 0.375 0.325 0.02%
0.05%
0.10%
0.20%
0.39%
0.78%
1.56%
3.13%
6.25%
12.50%
25%
50%
100%
Percentage of co‐efficient
Fig 3 – Average Genuine Acceptance Rate of proposed palm print identification methods using Walsh transform
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Dr H.B Kekre et al. / International Journal of Engineering Science and Technology Vol. 2 (12), 2010, 7372-7379 4.3 Walsh Transform The average genuine acceptance rate values (GAR) of proposed palm print identification techniques implemented using Walsh transform is plotted in figure-4. The figure shows the GAR values of considering only left palm print (WALSH L), only right palm print (WALSH R) and both (WALSH L_R) with average (AVG) of all these 3 GARs. The figure shows improvement in GAR values with reducing amounts of fractional coefficients up to 0.78%, indicating that fractional coefficients give better performance than all transformed coefficient data in Walsh transform. Further it can be observed that considering both left and right palm prints together increases the ruggedness of the system in all fractional coefficients as shown by higher GARs. It is further observed that 0.78% fractional coefficients of Walsh transformed palm print recognition proves to be the best. As well as the speed of palm print recognition improves with decreasing amount of fractional coefficients considered as feature vectors because of less number of comparisons needed in matching the query features with database image features resulting into faster palm print identification.[6]
WALSH R
WALSH L_R
6.25%
1.56%
WALSH L
AVG
0.75 0.7 GAR
0.65 0.6 0.55 0.5 0.45 0.4 0.02%
0.05%
0.10%
0.20%
0.39%
0.78%
3.13%
12.50%
25%
50%
100%
Percentage of co‐efficient
Fig 4 – Average Genuine Acceptance Rate of proposed palm print identification methods using Walsh transform
4.4 Slant Transform The average genuine acceptance rate values (GAR) of proposed palm print identification techniques implemented using Slant transform is plotted in figure-5. The figure shows the GAR values of considering only left palm print (SLANT L), only right palm print (SLANT R) and both (SLANT L_R) with average (AVG) of all these 3 GARs. It is seen that there is a gradual degradation in GAR values with reducing amounts of fractional coefficients. Further it can also be observed that considering both left and right palm prints together increases the ruggedness of the system in all fractional coefficients as shown by higher GARs. 50% of fractional coefficients of Slant transformed palm print recognition proves to be the best in terms of performance. [13]
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Dr H.B Kekre et al. / International Journal of Engineering Science and Technology Vol. 2 (12), 2010, 7372-7379 SLANT L
SLANT R
SLANT L_R
AVG
0.675 0.625 GAR
0.575 0.525 0.475 0.425 0.375 0.325 0.02%
0.05%
0.10%
0.20%
0.39%
0.78%
1.56%
3.13%
6.25%
12.50%
25%
50%
100%
Percentage of co‐efficient
Fig 5 – Average Genuine Acceptance Rate of proposed palm print identification methods using Slant transform
4.5 Hartley Transform The average genuine acceptance rate values (GAR) of proposed palm print identification techniques implemented using Hartley transform is plotted in figure-6. The figure shows the GAR values of considering only left palm print (HARTLEY L), only right palm print (HARTLEY R) and both (HARTLEY L_R) with average (AVG) of all these 3 GARs. The figure shows a gradual degradation in GAR values with reducing amounts of fractional coefficients. Further it can be observed that considering both left and right palm prints together increases the ruggedness of the system in all fractional coefficients as shown by higher GARs. 100% of fractional coefficients of Hartley transformed palm print recognition proves to be the best in terms of performance. HARTLEY L
HARTLEY R
HARTLEY L_R
AVG
0.674 0.624 GAR
0.574 0.524 0.474 0.424 0.374 0.324 0.02%
0.05%
0.10%
0.20%
0.39%
0.78%
1.56%
3.13%
6.25%
12.50%
25%
50%
100%
Percentage of co‐efficient
Fig 6 – Average Genuine Acceptance Rate of proposed palm print identification methods using Hartley transform
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Dr H.B Kekre et al. / International Journal of Engineering Science and Technology Vol. 2 (12), 2010, 7372-7379 4.6 Haar Transform The average genuine acceptance rate values (GAR) of proposed palm print identification techniques implemented using Haar transform is plotted in figure-7. The figure shows the GAR values of considering only left palm print (HAAR L), only right palm print (HAAR R) and both (HAAR L_R) with average (AVG) of all these 3 GARs. The figure shows a gradual degradation in GAR values with reducing amounts of fractional coefficients. Further it can be observed that considering both left and right palm prints together increases the ruggedness of the system in all fractional coefficients as shown by higher GARs. 0.78% of fractional coefficients of Haar transformed palm print recognition proves to be the best in terms of performance. [6] HAAR L_R
AVG
0.39%
HAAR R
1.56%
HAAR L 0.72 0.67
GAR
0.62 0.57 0.52 0.47 0.42 0.37 0.32 0.02%
0.05%
0.10%
0.20%
0.78%
3.13%
6.25%
12.50%
25%
50%
100%
Percentage of co‐efficient Fig 7 – Average Genuine Acceptance Rate of proposed palm print identification methods using Walsh transform
The comparison of the average genuine acceptance rates(GAR) of proposed palm print identification techniques implemented using Sine, Cosine, Walsh, Slant, Hartley and Haar transforms is plotted in figure-8. The figure shows the GAR values of considering only both left and right palm prints(DST L_R,DCT L_R,WALSH L_R,SLANT L_R,HARTLEY L_R,HAAR L_R). The figure shows improvement in GAR values of Sine, Cosine, Walsh and Haar transforms and degradation in GAR values of Slant and Hartley transform. Further it can be observed that considering both left and right palm prints together increases the ruggedness of the system in all fractional coefficients as shown by higher GARs. It is seen that 0.78% fractional coefficients of Walsh transformed palm print recognition proves to be better as compared to the other techniques. Even the speed of palm print recognition improves with decreasing amount of fractional coefficients considered as feature vectors because of less number of comparisons needed in matching the query features with database image features resulting into faster palm print identification.
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Dr H.B Kekre et al. / International Journal of Engineering Science and Technology Vol. 2 (12), 2010, 7372-7379 DCT L_R
WALSH L_R
50%
12.50%
DST L_R
SLANT L_R
HARTLEY L_R
HAAR L_R
0.734
GAR
0.684 0.634 0.584 0.534 0.484 0.434 0.02%
0.05%
0.10%
0.20%
0.39%
0.78%
1.56%
3.13%
6.25%
25%
100%
Percentage of co‐efficient
Fig 8 – Average Genuine Acceptance Rate of proposed palm print identification methods using Sine, Cosine, Walsh, Slant, Hartley and Haar transforms
5. Conclusion Thus we see that DCT, Walsh, DST and Haar transforms give a better performance as compared to Slant and Hartley with DCT and DST both performing the best. At 0.78% the GAR value of DCT is the highest. The next highest is that of DST, Haar followed by Walsh. The numbers of pixels are reduced from 100% to 0.78% and it is seen that DST, DCT, Haar and Walsh all perform their best at this level. While at the same time it can also be seen that Slant transform and to a smaller extent, Hartley transform consistently perform poorly as compared to the other transforms. 6. References [1] [2] [3] [4] [5]
[6]
[7] [8] [9] [10] [11] [12] [13] [14] [15]
Bong,D.B.L; Tingang R.N; Joseph.A(2010):“Palm Print Verification System”, Proceedings of the World Congress on Engineering 2010 Vol I, WCE 2010, June 30 - July 2, 2010, London, U.K. http://www.biometricscatalog.org/NSTCSubcommittee/Documents/Palm%20Print%20%20Recognition.pdf (last referred on 29 Nov 2010) http://www.ccert.edu.cn/education/cissp/hism/039-041.html (last referred on 29 Nov 2010) http://www.digital-systems-lab.net/doc/shk_hartleytransform.pdf (last referred on 29 Nov 2010) Dr.Kekre.H;Thepade.S;Maloo.A(2005):“Image Retrieval using Fractional Coefficients of Transformed Image using DCT and Walsh Transform”, International Journal of Engineering Science and Technology (IJEST), Volume 2, Number 4, 2010, pp.362371.(ISSN:0975-5462).Available online at http://www.ijest.info/docs/IJEST10-02-04-05.pdf. Dr.Kekre.H;Thepade.S,Maloo.A(2008):“Performance Comparison of Image Retrieval Using Fractional Coefficients of Transformed Image Using DCT, Walsh, Haar and Kekre’s Transform”, CSC International Journal of Image Processing (IJIP), Volume 4, Issue 2, pp 142-157, Computer Science Journals, CSC Press, www.cscjournals.org Dr Kekre.H;Thepade.S(2009):"Improving the Performance of Image Retrieval using Partial Coefficients of Transformed Image”, International Journal of Information Retrieval, Serials Publications, Volume 2, Issue 1, pp. 72-79 (ISSN: 0974-6285) Dr Kekre.H;Thepade.S;Sarode.T(2008):“DCT applied to column mean and row mean vectors pof image for fingerprint identification”, International Conference on computer networks and security,Vishwakarma Institute Of Technology,Pune. IIT Delhi Touchless Palmprint Database (Version 1.0), http://web.iitd.ac.in/~ajaykr/Database_Palm.htm Kumar.A;Wong.D;Shen.H;Jain.A(2003):“Personal Verification Using Palm print and Hand Geometry Biometric. Proc. of 4th International Conference on Audio-and Video-Based Biometric Person Authentication (AVBPA)”, Guildford, UK. Parashar.S;Vardhan.A;Patvardhan.C;Kalra.P “Design and Implementation of a Robust Palm Biometrics Recognition and Verification System”, Sixth Indian Conference on Computer Vision, Graphics & Image Processing. Prof.Dr. Saad E.S.,M;Prof.Dr.Eladawy M.I;Eng Aly R.F et al(2008):“Person Identification Using Palm prints”. http://ralph.cs.cf.ac.uk/papers/Geometry/TruncationSlant.pdf (last referred on 29 Nov 2010) Zhang.D; Kong.W; You.J;Wong.M(2003):“Online Palmprint Identification”, IEEE Transactions on Pattern Analysis and Machine Intelligence. 25: p. 1041-1050. Zhang.D;Wu.X; K. Wang (2006)“Palm line extraction and matching for personal authentication” IEEE Transactions on Systems, Man, and Cybernetics, 2006. 36: p. 978-987.
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