Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics Bangkok, Thailand, February 21 - 26, 2009
Palmprint Image Enhancement Using Phase Congruency Yunyong Punsawad and Yodchanan Wongsawat Department of Biomedical Engineering Mahidol University 25/25 Puttamonthon Sai 4 Rd. Salaya, Nakornprathom 73170, Thailand
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[email protected] Abstract-This paper proposes the use of the phase congruency to enhance the palmprint image used for palmprint identification. By using phase congruency, the palmprint lines which act like the edges in the palmprint image are detected. The resulting phase image is shown as the enhanced palmprint image. Comparing with the previous palmprint enhancement method that uses the phase symmetry proposed by Kovesi et al. (1997), the proposed method is significantly less sensitive to the textures which are not the palmprint lines in the palmprint image. Index Terms – Palmprint, Phase Congruency, Image Enhancement
I. INTRODUCTION Palmprint identification is a subcategory of biometrics identification, which can efficiently used to identify the people. Palmprint-based identification is currently a potential alternative human identification method of a well known fingerprint-based identification. For example, if the hand of the identified person is dirty, the accuracy of fingerprint-based identification is distorted, while the palmprint-based identification still yields high identification accuracy [1]. In order to achieve high identification accuracy, all components of the scanned palmprint image need to be enhanced, i.e. palmprint lines [1, 2], textures [3] and handgeometry features [4]. Specifically, each component needs to be enhanced and employed in order to efficiently identify the people. In this paper, we focus specifically on finding the efficient palmprint lines enhancement method which is the main component on palmprint-based identification. Conventionally, Sobel operator, Prewitt operator, and Canny’s technique are used to roughly enhance the palmprint lines [5]. Recently, edginess [6] as well as the phase symmetry [7] is employed as the new efficient palmprint lines enhancement method. However, these edge detection methods are highly sensitive to the texture area. Phase congruency is another efficient edge detection method proposed in [8]. It is invariant to changes in image brightness. Therefore, phase congruency provides an absolute measure of the features of interest. Hence, in this paper, we study the effect of phase congruency proposed in [8] to the palmprint image. The parameters used for calculating the phase congruency are calibrated in order to enhance the
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palmprint lines. The resulting enhanced image is shown to be less sensitive to some non-useful textures. This paper is organized as follows. Section II introduces the phase congruency method. Section III describes the proposed palmprint image enhancement method. In section IV, the simulation results on palmprint image enhancement using phase congruency are demonstrated. Finally, Section V concludes the paper. II. PHASE CONGRUENCY This section aims to describe a background on calculating the phase congruency. By observation, frequency components of an image are normally in-phase at the position of edges [8]. Hence, if we would like to know the positions of the edges in the image, one efficient method is to find the position that the frequency components of the image are in-phase. This method is employed in order to quantitatively describe how much the frequency components of the interested areas in the image are in phase. This method is called “phase congruency”. The phase congruency can be calculated via many methods, e.g. Fourier series and wavelet transform. Kovesi [8] developed the measure of phase congruency that provides good localization and also includes the noise compensation. Phase congruency (PC) can be defined as PC ( x) =
∑ W ( x) ⎣ A ( x)Δ Φ ( x) − T ⎦ , ∑ A ( x) + ε n
n
n
n
(1)
n
where many functions need to be defined as follows: 1) W (x ) is the weighting function that devalues phase congruency at the location where the spread of frequency response is narrow, which can be defined as
W ( x) =
1 1+ e
γ ( c − s ( x ))
.
(2)
2) n is scale of the wavelet transform (log Gabor wavelet transform), c is the cutoff value or filter response spread, γ is a factor used to control the sharpness of the cutoff, ε is a small constant, and T is the estimated noise influence. 3) Frequency response spread s(x) can be measured by
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⎛ ∑n An ( x) ⎞ (3) ⎜ ⎟. ⎜ ⎟ x ε ( ) + ⎝ Amax ⎠ 4) An(x) is the amplitude of the wavelet transform at a given scale n, Amax(x) is An(x) which has the maximum response at position x, and N is the total number of scale being used. 5) Phase deviation function ΔΦn(x) can be calculated by s( x) =
1 N
ΔΦ n ( x) = cos(φn ( x) − φ ( x)) − sin(φn ( x) − φ ( x)) . 6)
(4)
φn (x) is the phase angle, φ (x) is the overall mean of
the phase angle. In order to efficiently employ the phase congruency to a specific application, some parameters mentioned above need to be carefully adjusted. III. PALMPRINT IMAGE ENHANCEMENT A. Previous method This section aims to review one of the efficient previous palmprint image enhancement method proposed in [9]. The key of this method is using the phase symmetry proposed in [7]. By observing that at the location that the image is symmetric, the local phase will allow only the evensymmetric filters to respond. On the other hand, if the image is asymmetric only the odd-symmetric filters will be responding. By assuming that the edges of palmprint lines are either symmetric or asymmetric, the phase symmetry feature extraction method is efficiently employed to enhance the palmprint lines. B. Proposed method Similar to A, if we assume that the palmprint lines (edges) are the locations that the sinusoidal bases are in phase, phase congruency is an efficient tool used to detect the palmprint lines. In addition, the resulting phase image calculated via the phase congruency is used as the enhanced palmprint image. The proposed palmprint lines enhancement method can be summarized as follows: 1.
Convert an RBG color image to a grayscale image,
2.
Apply phase congruency to the resulting grayscale image,
3.
Adjust the parameters used to calculate phase congruency.
order to verify the merit of our proposed method, we compare our enhancement results with the phase-based method in [9]. According to the palmprint image in [10], the parameters used to calculate the phase congruency are adjusted in order to reveal clear palmprint lines. The ratio between the angular spacing of the filters and angular standard deviation of the Gaussians is set to 1.2. This results in coverage of the spectrum that varies by less than 1%. A noise compensation parameter, T, of 2.0 is used. The frequency spread weighting function cutoff fraction, c, is set to 0.4, and the gain parameter, γ is set to 10. The value of ε, the small constant used to prevent the division by zero in the case where local energy in the image becomes very small, is set to 0.01. In this paper, we evaluate our proposed palmprint enhancement method via two palmprint images with different shapes and brightness levels (Figs. 1(a) and 3(a)). In order to evaluate the efficiency of the proposed method, we compare the resulting enhanced images using the proposed method (Figs. 1(c) and 3(c)) with the enhanced images using phase symmetry method (Figs. 1(b) and 3(b)). Figs. 2(a)-(c) and Figs. 4(a)-(c) are the zoomed version of Figs. 1(a)-(c) and Figs. 3(a)-(c), respectively. Comparing with the enhancement via the previous method (Figs 1-4(b)), the enhanced images obtained by our proposed method (Figs 1-4(c)) can efficiently extract the palmprint lines and thus enhance the palmprint images. In particular, we can still see a lot of non-palmprint lines in the enhanced images obtained by the previous work, while the non-palmprint lines are dramatically reduced when the proposed method is used. Moreover, according to Figs. 1(c) and 3(c), phase congruency can also efficiently enhance the geometry of the palmprint which can be used as another feature in human identification. V. CONCLUSIONS In this paper, we have presented the image enhancement method specifically for palmprint image. Phase congruency is used to obtain the phase information for detecting and enhancing the edges of palmprint lines. Simulation results show that, besides the quality of the proposed enhanced image, the proposed method is robust to both brightness and non-palmprint lines. Automatic method for adjusting the parameters used in phase congruency (for various conditions of the images) needs more investigation. REFERENCES [1] D. Zhang, W.-K. Kong, J. You, and M. Wong, “Online palm print identification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol.25, pp.1041-1050, 2003.
IV. SIMULATION RESULTS In this paper, we employ our proposed palmprint enhancement method to detect the palmprint lines. The palmprint images used in this paper are obtained from the database in [10]. As described in Section IIIA, phase symmetry method [9] is employed to enhance the palmprint lines. This method can efficiently enhance the palmprint lines compared with other magnitude-based methods. Therefore, in
[2] J. You, A. W.-K. Kong, D. Zhang, and K. H. Cheung, “On hierarchical palmprint coding with multiple features for personal identification in large databases,” IEEE Trans. Circuits Syst. Video Techn., vol.14 pp.234-243, 2004.
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Fig. 1 (a) Original palmprint image, (b) Palmprint image enhanced using phase symmetry method, and (c) Palmprint image enhanced using the phase congruency method.
Fig. 2 (a) Original palmprint image, (b) Palmprint image enhanced using phase symmetry method, and (c) Palmprint image enhanced using the phase congruency method.
[3] X. Wu, F. Zhang, K. Wang, and D. Zhang, “Fusion of the textural feature and palm-lines for palmprint authentication,” In Proceedings of ICIC, vol.1, pp.1075-1084, 2005. [4] A. Kumar and D. Zhang, “Combining fingerprint, palmprint and handshape for user authentication,” In Proceedings of ICPR, vol.4, pp.549552. [5] R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB, Pearson Prentice Hall, Upper Saddle River: New Jersey, 2004.
[6] P. Kumar, S. Das, and B. Yegnanarayana, “One dimensional processing of images,” In Proceedings of International Conference on Multimedia Processing Systems, pp. 181-185, 2000. [7] P. Kovesi, “Symmetry and asymmetry from local phase,” Tenth Australian Joint Conference on Artificial Intelligence, pp. 185-190, 1997.
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Fig. 3 (a) Original palmprint image, (b) Palmprint image enhanced using phase symmetry method, and (c) Palmprint image enhanced using the phase congruency method.
[8] P. Kovesi, “Image Features from Phase Congruency,” Videre: journal of Computer vision Research, vol. 1, MIT Press, 1999: MATLAB Software, Available [Online] at http://www.csse.uwa.edu.au/~pk/ Research/MatlabFns/index.html.
Fig. 4 (a) Original palmprint image, (b) Palmprint image enhanced using phase symmetry method, and (c) Palmprint image enhanced using the phase congruency method.
[9] P. Hennings, M. Savvides, and B. V. K. Vijaya Kumar, “Palmprint recognition with multiple correlation filters using edge detection for class-specific segmentation,” In proceedings of IEEE workshop Automatic Identification advanced technologies, pp.214-219, 2007. [10] Palmprint Database Available [Online] advancedsourcecode.com/palmprint.asp
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