Integrating Palmprint and Fingerprint for Identity Verification

Transcription

1 2009 Third nternational Conference on Network and System Security ntegrating Palmprint and Fingerprint for dentity Verification Yong Jian Chin, Thian Song Ong, Michael K.O. Goh and Bee Yan Hiew Faculty of nformation Science and Technology (FST), Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, 75450, Melaka, Malaysia, {yjchin, tsong, michael.goh, Abstract: n this paper, we propose a multimodal biometrics system that combines fingerprint and palmprint features to overcome several limitations of unimodal biometrics such as the inability to tolerate noise, distorted data and etc. and thus able to improve the performance of biometrics for personal verification. The quality of fingerprint and palmprint images are first enhanced using a series of preprocessing techniques. Following, a bank of 2D Gabor filters is used to independently extract fingerprint and palmprint features, which are then concatenated into a single feature vector. We conclude that the proposed methodology has better performance and is more reliable compared to unimodal approaches using solely fingerprint or palmprint biometrics. This is supported by our experiments which are able to achieve equal error rate (EER) as low as 0.91% using the combined biometrics features. 1. ntroduction Nowadays, unimodal biometrics such as fingerprint is widely used for identity verification. However, many unimodal biometrics systems suffer from limitations such as the inability to tolerate deformed data due to noise deformed data from the sensor device, distorted signal by environmental noise, and variability of an individual s physical appearance and pattern over time. Within this context, multimodal biometrics systems are able to solve some of these limitations by combining information from multiple biometrics sources [1]. The degree of fusion in a typical multimodal biometrics system can be divided into four levels, namely, data level, feature level, match score level, and decision level. To date, many researchers have focus on match score level fusion as it is relatively easier to access and combine the scores produced by different modalities. n this context, Kumar et. al. [2] proposed to fuse fingerprint, palmprint, and hand geometry at score level. The individual match score of the three modalities were combined using sum rule. Their proposed method was able to achieve an EER of 3.53%. As the feature set grows richer in biometric information, feature level fusion began to receive more and more attention from researchers, as it is able to produce better verification results. Son et. al. [3] applied Direct Linear Discriminant Analysis (DLDA) to fuse iris features and face features. The main purpose of DLDA is to discard the null space of between-class scatter while maintaining the null space of within-class scatter. Li et. al. [4] proposed to fuse palmprint, hand geometry, and knuckle-point biometrics using Kernel Principle Component Analysis (KPCA). KPCA is able to transform the original sample space into higher dimensional space by using a nonlinear mapping of every sample. Then, PCA is used to obtain a smaller feature space. n this paper, we present a framework to combine fingerprint and palmprint biometrics at feature level. We first apply a 2D discrete wavelet transform (2D- DWT) to decompose the images into lower resolution before performing feature extraction. mage decomposition using 2D-DWT is able to conserve the energy signal and redistribute them into a more compact form. Subsequently, we adopt a Gabor filter as the feature extractor for both biometrics, as they share some common characteristics such as ridges. Finally, the proposed feature level fusion method is utilized to combine the extracted fingerprint and palmprint images. The block diagram of our proposed fusion framework is shown in Figure 1. The outline of this paper is as follows: Section 2 describes the image preprocessing algorithm. Section 3 illustrates the fusion and feature extraction method. Section 4 depicts the experimental results and is followed by our conclusions in Section /09 $ EEE DO /NSS

2 Figure 1 Block diagram of the proposed fusion framework nput fingerprint image Cropped image mage filtering + enhancement by STFT 2D DWT LL subband nput palmprint image Cropped image mage filtering 2D DWT LL subband Figure 2 Preprocessing of palmprint images and fingerprint images

3 2. mage Preprocessing We applied only Gaussian low pass filter to smoothen the palmprint images. n addition to Gaussian filter, short time Fourier transform (STFT) analysis is adopted to enhance fingerprint images quality. Figure 2 provides an overview of the image preprocessing step. where, n denotes the resolution level, h and g denote the decomposition low-pass and high-pass filters, respectively. Two-dimensional (2D) DWT for 2D signal such as images can be implemented by performing 1D DWT in each signal dimension. An image is decomposed into four frequency sub-bands at each resolution level n by applying 2D DWT. The resulted four sub-bands are, an approximation sub-band (LL n ), and three detailed subbands (HL n, LH n, and HH n ). 2.1 Region of nterest (RO) Extraction The RO of palmprint images is located by using the right angle coordination system [5]. Subsequently, the RO of each image is resized to pixels. Meanwhile, the core point of each fingerprint image is manually located and cropped into pixels with the core point as center. 2.2 Short Time Fourier Transform (STFT) Analysis We employ STFT analysis proposed in [6] to enhance the fingerprint images. Figure 3 shows the fingerprint image before and after the STFT analysis is applied. (a) (b) Figure 3 Fingerprint images (a) before enhancement by STFT analysis, (b) after enhancement by STFT analysis. 2.3 Wavelet Transform Wavelet Transform (WT) is used to decompose images into different frequency components. With the lower resolution of each component, computational complexity is reduced. n this paper, we use WT to decompose the enhanced palmprint images and fingerprint images into lower resolution representation. Generally, 1D DWT of a signal ca can be obtained by convolving it with decomposition filters, n+ 1 n ca j = h i i 2 j cai n+ 1 cd j = gi 2 j cai i n (1) (2) (a) (b) Figure 4 (a) 1-level discrete wavelet decomposition of fingerprint image and (b) palmprint image. 3 Feature Extraction 3.1 2D Gabor Filter Palmprint and fingerprint share some common characteristics such as creases and ridges. Other palmprint characteristics are principle lines and wrinkles. A bank of 2D Gabor filters is used to filter palmprint and fingerprint images in different directions to highlight these characteristics and remove noises [7]. A 2D Gabor filter has the following form in the image domain (x,y): 2 2 x' + y' G( x, y, f, θ ) exp( 2 2σ ) cos(2πfx' ) = (3) where x ' = x cosθ + y sinθ and =, f is the frequency of the y ' x sin θ + y cosθ sinusoidal plane wave along the directionθ from the x- 2 axis, σ is the standard deviation of the Gaussian envelope. Our experimental results of unimodal 2 biometrics show that f = 10, σ = 16, and π θ = appears to be the best combination

4 3.2 Normalization The filtered images are normalized to the same domain using the following method: G ( x, y) µ σ ( x, y) = (4) where ( x, y) denotes the pixel intensity at coordinate (x,y), µ denotes the intensity mean, and σ denotes the intensity standard deviation. Normalization is important as the filtered palmprint and fingerprint images may not share the same intensity domain. 3.3 Feature Level Fusion We combine the normalized LL sub-band images and divide it into none overlapping blocks of size m n pixels each. Then, the resulting magnitude will be converted to a scalar number by calculating its standard deviation value. The size of each block is carefully chosen, so that no repeated feature is extracted. At last, a feature vector with 8 N N sub-gabor features is extracted from each image, where N denotes the number of rows and columns. The normalized images are fused as shown below: F i P i Figure 5 Arrangement of normalized images Fi: normalized LL sub-band of fingerprint image at index i, Pi: normalized LL sub-band of palmprint image at index i. Figure 6 sub-gabor feature is obtained by calculating the standard deviation of each dotted-line-block (m n pixels). P i F i 4.0 Experiment Testing 4.1 Databases The proposed method is evaluated on two different fingerprint databases and two different palmprint databases. The following are the denotations and details of the databases used: Fingerprint Databases F1: Database from [8]. Also known as the FVC2004 database. The fingerprint images are acquired by using an optical sensor and it contains 100 subjects with 8 images for each subject. F2: B.Y. Database from [9]. A digital camera is used to capture the fingerprint images. There are 103 subjects with 10 images for each subject. Palmprint Databases P1: Database from [10]. t has 7750 palmprint images from 386 subjects captured using a contact-based palmprint acquisition device. P2: Database from [11]. t has contact-less palmprint images collected from 208 subjects using a contact-less device and consist of 5160 cropped palmprint images. n our experiments, we randomly selected eight fingerprint and eight palmprint images of each subject in the databases. The region of interest (RO) of the fingerprint and palmprint images are first cropped and standardized into pixels. The core point of the fingerprint images in F1 and F2 are determined manually and the RO is extracted. For palmprint databases, we adopted the technique used by Zhang et. al. [5] to extract RO in P1 images, whereas, the palmprint images in P2 database have been precropped into the standard form for our experiments. Two sets of experiments are conducted. The first set of experiments is to obtain the equal error rate (EER) of the unimodal palmprint and fingerprint biometrics separately. The experiment used the preprocessing and feature extraction techniques defined in Section 2 and Section 3 respectively. n the second set of experiments, the proposed fusion technique is applied and evaluated using the following combinations {F1, P1}, {F1, P2}, {F2, P1}, {F2, P2}. Three feature vectors of different lengths, i.e. 200 (5x5x8), 392 (7x7x8), and 1800 (15x15x8), are extracted from each of the fused fingerprint and palmprint images. The features of both set of experiments are then classified

5 using Euclidean distance to determine the performance of the proposed method. 4.2 Experimental Results For experiment on unimodal biometrics, the processed images are not decomposed into smaller resolution. We extract 200 sub-gabor features from each image, and then classify them accordingly to their Euclidean distance. As for fusion experiment, the Daubechies Order 2 wavelet basis with level 1 decomposition of WT is applied to reduce the image resolution Experiment 1 From Table 1, we notice that the proposed fusion method offers better performance in term of EER compared to unimodal biometrics system. For instance, EER for database F1 is 15.19%. After image of F1 are fused with P1, the EER dropped to 5.59%. On the other hand, the fusion of F1 and P2 is capable of minimizing the EER to 2.97% Table 1 Performance comparison of unimodal biometrics system with proposed fusion method. Figure 7 Receiver Operating Characteristic (ROC) curves. Table 2 Performance comparison of fusion at different feature lengths. Database Fused with FAR (%) FRR (%) EER (%) Database F1 + P1 No. of Block sub- FAR FRR EER size Gabor (%) (%) (%) (pixels) features 8x5x5 30x x7x7 20x x15x15 10x F1 F P P P P F1 + P2 8x5x5 30x x7x7 20x P1 F x15x15 10x F F2 + P1 8x5x5 30x x7x7 20x P2 F x15x15 10x F F2 + P2 8x5x5 30x x7x7 20x x15x15 10x As shown in figure 7, our proposed fusion method performs better than unimodal biometrics for both fingerprint and palmprint biometrics. The ROC curve of the fusion method is above that for unimodal fingerprint and palmprint system Experiment 2 Next, we investigate the performance of our proposed fusion feature method for various feature lengths. The block sizes are set at pixels, pixels, and pixels, resulting in feature vectors of length 200, 392 and 1800 dimensions

6 bimodal biometrics makes it harder for adversaries to succeed in an attack as they have to spoof both biometrics simultaneously. (a) (b) (c) (d) Figure 8 (a) Filtered image and example of feature vectors: with block size of (b) pixels, (c) pixels, (d) pixels From Table 2, it can be observed that, the EER starts to fall as more sub-gabor features are extracted from each fused image. However, this is not the case when database F2 is fused with database P1. When 392 sub- Gabor features are extracted, the EER increases to 4% from 2.95%. The EER is 3.85% if 1800 sub-gabor features are extracted. Table 3 summarizes the performance of different feature level fusion methods that employ palmprint biometrics. Table 3 Performance summary of different feature level fusion methods. Biometric traits and FAR FRR EER Ref. features (%) (%) (%) [12] [4] [13] Hand geometry + palmprint Hand geometry + palmprint + knuckle print Hand geometry + palmprint * Palmprint + fingerprint * proposed method. 5 Conclusion n this paper, we presented a novel feature level fusion method for palmprint and fingerprint biometrics. WT is applied to reduce the image resolution while retaining important palmprint and fingerprint characteristics. Low frequency sub-band images were derived and used as it is only quarter size of the original image and contains most of its energy content. Hence, the computational complexity is significantly reduced by processing a lower resolution image. Our proposed fusion method combines unique characteristics of palmprint and fingerprint to enable better discrimination against imposters. n addition, it requires only the same amount of memory for storage purposes. The EER can be further decreased when more unique features are extracted. Besides that, Acknowledgment This research was supported by the Malaysia e-science Fund ( SF0113). References [1] Ross, A., Jain, A.K., (2003). nformation Fusion in Biometrics, Pattern Recognition Letter, 24 (13), pp [2] Kumar, A., Zhang, David, (2006). Combining Fingerprint, Palmprint and Hand-shape for User Authentication, 18th nternation Conference on Pattern Recognition, CPR 2006, 4, pp [3] Son, B., Ahn, J.-H., Park, J.-h., Lee, Y., (2004). dentification of Humans Using Robust Biometric Features, Lecture Notes in Computer Science, pp [4] Li, Q., Qiu, Z., Sun, D., (2005). Feature-Level Fusion of Hand Biometrics for Personal Verification Based on Kernel PCA, Lecture Notes in Computer Science, 3832/2005, pp [5] Zhang, David., Kong, W,, You, J., and Wong, Michael, (2003). Online Palmprint dentification, EEE Transactions on Pattern Analysis and Machine ntelligence, 25(9), pp [6] S. Chikkerur, A.C., and V. Govindaraju, (2005). Fingerprint mage Enhancement using STFT Analysis, nternational Conference on Advances in Pattern Recognition, [7] Jain, A.K., Prabhakar, S., Hong, L. and Pankanti, S., (1999). FingerCode: A Filterbank for Fingerprint Representation and Matching, EEE Computer Security Conference on Computer Vision and Pattern Recognition, 2, pp [8] FVC2004 Fingerprint Database, [9] Hiew, B.Y., Andrew, T.B.J., Pang, Y.H., (2007). Touch-less Fingerprint Recognition System, EEE Workshop on automatic dentification Advanced Technologies, [10] PolyU Palmprint Database, [11] Michael, G.K.O., Connie, T., Andrew, T.B.J. and David, N.C.L., (2006). A Fast Palm Print Verification System, Proceedings of the nternational Conference on Computer Graphics, maging and Visualisation, [12] Han., C.C., (2004). A Hand-based Personal Authentication using Coarse-to-fine Strategy, mage and Vision Computing, 22(11), [13] Kumar, A., Wong, David, C. M., Shen, Helen, C., Jain, A. K., (2003). Personal Verification using Palmprint and Hand Geometry Biometry,