1. A Comparison in Handmetric between Quaternion Euclidean Product Distance and Cauchy Schwartz Inequality Distance Di Liu Dong-mei Sun Zheng-ding Qiu Institute of Information Science, Beijing Jiaotong University, P.R.China 100044

2. Outline 1.Introduction 2.QEPD & CSID 3.Comparison by Experiment 4.Conclusion

3. 1.Introduction This paper proposes a comparison in handmetrics between Quaternion Euclidean product distance (QEPD) and Cauchy-Schwartz inequality distance (CSID), where "handmetrics" refers to biometrics on palmprint or finger texture.

4. 1.Introduction All two distances could be constructed by quaternion which was introduced for reasonable feature representation of physical significance, i.e. 4-feature parallel fusion. Simultaneously, such quaternion representation enables to avoid incompatibleness of multi-feature dimensionality of quaternion fusion.

5. 1.Introduction We give a comparison on experimental aspects for providing a conclusion which algorithm is better. 2.QEPD & CSID

6. Quaternion Euclidean Product Distance (QEPD) Quaternion is a non-commutative extension of complex numbers, which first described by the Irish mathematician Sir William Rowan Hamilton in 1843 and applied to mechanics in three-dimensional space [4].

7. Quaternion Euclidean Product Distance Quaternion properties Conjugate Modulus Assume

8. Quaternion Euclidean Product Distance Equality Addition Multiplication Inner Product Quaternion Euclidean Product

9. Quaternion Euclidean Product Distance QEPD Consider that when, QEP is equal to, i.e. the square of modulus Assume two quaternions P and Q, which the former is from an arbitrary pixel corresponding to 4 separable wavelets decomposition sub-image from template,

10. Quaternion Euclidean Product Distance Ideally, if such that, where is a particular case of QEP. We estimate the difference between the template quaternion P and the test one (Q) by QEPD, where

11. Cauchy-Schwartz inequality distance Autocorrelation for signal processing. Autocorrelation is used frequently in signal processing for analyzing functions or series of values, such as time domain signals [7]. Let x(t) as a continuous signal sequence, where denotes x(ti); x(tj) as the sequence at time ti; tj respectively. Define the autocorrelation in the case of serial signals in the equation 10.

12. Cauchy-Schwartz inequality distance In the case of discrete signal sequence, we define as autocorrelation for This term also has many properties, e.g. symmetry, when x(n) is a real sequence,

13. Cauchy-Schwartz inequality distance when x(n) is a complex one In addition, a quaternion wavelet feature representation for parallel fusion is viewed as a real signal sequence. Under a similar image acquisition condition, there is more or less a distinction among sample images. To this end, the images are treated as two dimensional stochastic signal.

14. Cauchy-Schwartz inequality distance Cauchy-Schwartz inequality distance. Consider two quaternions, which the former is constructed by the same pixel corresponding to 4 separable wavelets decomposition coefficients from the template, and the latter from the tester, in which If template and tester one belong to the same person, will be more similar with

15. Cauchy-Schwartz inequality distance than that from different persons, i.e. modulus of is smaller than that from different persons. Thus we obtain Thus we view P and Q as real signal sequences in sense of discrete-time signals, i.e. transform quaternion P and Q into forms of and

16. Cauchy-Schwartz inequality distance in which x(p) and x(q) are discrete sequences at time p and q respectively. According to the autocorrelation discussed above, the equation (12) can be rewritten as Where p = q -, time q can be viewed as a time delay to p. Now evolve the equation (12)

17. Cauchy-Schwartz inequality distance Replace (15) with (14), we obtain It is easily found that the left side of the equation above is larger than 0. To this end, it is accommodate to set this as the distance of the pixel for 4 sub-images with a reasonable physical significance.

18. Cauchy-Schwartz inequality distance Where k is number of pixel in such sub- images, n is the component number of discrete signal sequence, e.g. k = 4 because of the quaternion. Notice that, the sum of, evidently has a characteristic of

19. 3.Comparison by Experiment Database BJTU-HA biometric database, an inherited collection work by Institute of Information Science, Beijing Jiaotong University, is utilized for our palmprint and middle finger texture verification experiment. It contains totally 1,500 samples from 98 person's palmprint and middle finger with different illumination conditions.

20. QEPD computation

24. CSID computation

26. ROC curve for 4 distances

27. CONCLUSION This paper mainly proposes a comparison by two different discriminate distance, Quaternion Euclidean Product Distance (QEPD) and Cauchy-Schwartz Inequality Distance (CSID), in order to solve space incompatibleness and curse of dimensionality in a non-subspace means. From the experiments, we can safely provide two conclusions: (1) the algorithm of QEPD is better than that of CSID, (2) finger texture, this novel biometric is a better discriminative than traditional biometric palmprint for QEPD or CSID.

28. Reference [1] D. Liu, D.M. Sun and Z.D.Qiu, Wavelet decomposition 4-feature parallel fusion by quaternion euclidean product distance matching score for palmprint verification, 9th International Conference on Signal Processing, pp.2104-2017, 2008. [2] D. Liu, Z.D. Qiu and D.M. Sun, 2D wavelet decomposition feature parallel fusion by quaternion euclidean product distance matching score for middle finger texture verification, In press, 2008 International Conference on Bio-Science and Bio-Technology, Dec 13-15, 2008. [3] A. Ross, and A. Jain, Information fusion in biometrics, Pattern Recognition Letters, Volume 24, Issue 13, 2115-2125, 2003. [4] F. Lang, J. Zhou, Y. Bin, E. Song and F. Zhong, Quaternion based information parallel fusion and its application in color face detection, 9th International Conference on Signal Processing,Volume 3, 16-20, 2006 [5] L. Wen, Quaternion Matrix, ChangSha, China: National University of Defense Technology Press, 96-100, 2002 [6] J.G.Proakis and D.G.Manolakis, Digital Signal Processing, Pearson Education Press, 2007. [7] A. Jain, K. Nandakumar and A. Ross, Score normalization in multimodal biometric systems, Pattern Recognition, Volume 38, Issue 12, 2270-2285,2005. [8] C. Sanderson and K. K. Paliwal, Identity verification using speech and face information, Digital Signal Processing, Volume 14, Issue 5, 449-480,2004. [9] A.K. Jain and A. Ross, Multibiometric systems, Communication of the ACM, Vol.47, No.1,34- 40,2004. [10] J.Yang, D.Zhang and J.Lu ， Feature fusion: parallel strategy vs. serial strategy, Pattern Recognition ， 36 (6) ， 1369-1381,2003. [11] D.Liu, Z.D. Qiu and D.M. Sun, SIFT Feature-Based Face Verification by Bag-of-words Vector Quantization, submitted, Institute of Electronics, Information and Communication Engineering, transactions on information and systems.

29. That’s end! Thank you!