A multimodal biometric system is applied to recognize individuals for authentication using neural networks. In this paper multimodal biometric algorithm is designed by integrating iris, finger vein, palm print and face biometric traits. Normalized score level fusion approach is applied and optimized, encoded for matching decision. It is a multilevel wavelet, phase based fusion algorithm. This robust multimodal biometric algorithm increases the security level, accuracy, reduces memory size and equal error rate and eliminates unimodal biometric algorithm vulnerabilities.
Multimodal Biometrics Algorithm is adapted to the application where high level security is required and applied for authentication mode. Multimodal Biometrics is an integration of more than one biometric trait to enhance security. This paper presents a matching algorithm for the person who claims the identity for authentication. Multimodal Biometrics algorithm is more robust and the integration of many unimodal biometrics makes the system high secured. Identifying and Verifying a human being can be done using their physiological and behavioral characteristics. Every individual is identified by: something you possess such as ID card, Smart Card etc., something you know such as PIN, passwords etc., and something unique about you such as biometric traits [
Every year the amount spent for the recovery of passwords is increased. After the twin tower incident on Sep 11, 2011, all realized the need for security [
This paper integrates four multimodal biometrics: Iris, Finger Vein, Palm Print and Face [
Iris recognition is unique and has strong unimodal characteristics in identifying a human being in spite of all security threats. Iris is a Physiological Biometrics. It is done by measuring the distance between pupil the boundary
of Iris. It is done by measuring the distance between pupil the boundary of Iris. It is done by measuring the distance between pupil the boundary of Iris. Both inner and outer boundaries are not concentric circles. Preprocessing is done for localization of iris image and is shown in
Each sub image of iris is represented by wavelet coefficients to generate iris binary code for recognition. Iris recognition module contains segmentation, feature code generation. In segmentation iris localization and normalization is done. In feature code generation phase 64 wavelet packets are generated. The mean energy distribution allows evaluating which packets are used to compute normalized adapted threshold for iris code generation. The energy measure Ei for a wavelet packet sub image Wi can be computed as
In Amsterdam Schiphol Airport (UK) Iris Recognition is used for immigration [
Finger Vein recognition is invisible to naked eye, difficult to forge, steal. It is reliable, accurate and unique in identical twins, triplets, quadruplets, quintuplets. Burn, abrasions, cuts do not affect the ridge structure and vein. It can be taken only from live body, so the subject is ensured alive. Preprocessing of Finger vein takes segmentation, enhancement, filtering, thinning.
for image denoising [
It is Physiological biometric trait. This algorithm uses 2D Discrete Fourier transform in phase based recognition system. The Principal Component Analysis, Local Binary Pattern Histogram hybrid algorithm [
The methods to recognize face are Principal Component Analysis (PCA), Local Feature Analysis (LFA), Eigen Face Values, Template based recognition, Euclidean distance, Bunch graph matching [
one of the commercialized biometric recognition algorithm. When is integrated with other biometric traits, it performs well. It is very fast in recognition. It is implemented on CASIA (Institute of Automation of the Chinese Academy of Sciences) Database v 2.0.
Normalization brings compatibility between multimodal biometric traits. Individual traits are not homogeneous. Gray scale matrix are used as training data for neural networks. Cartesian polar coordinates are used in normalization. After normalization the scores becomes convenient transformation for fusion.
Score level fusion rules are constructed in order to achieve more accuracy and complexity for other vulnerabilities. It consumes lower communication bandwidth. It is easy to process and provides optimal performance. Bayesian Classifier based fusion rules are constructed. There are two types of rules exists: AND and OR. It can be easily combined with other multimodal biometrics and also accessible.
At all levels this optimization is adapted to gain best solution. Particle Swarm Optimization is applied for reducing the search space. It is proven that more efficient compared with Genetic Algorithm. PSO is the combination of deterministic and probabilistic rules. Computational cost is affordable when compared to Genetic algorithm. Neural Network is adapted for the nature of adaptive learning, self-organization, and fault tolerance. Finger vain Recognition, threshold values and the respective FAR and FRR is shown in
・ Integration of multimodal biometric is challenging. So it leads to complexity in memory and computations.
・ It is very hard to implement in real time since different sensor devices compatibility and instances of the devices must match in parallel processing time.
・ Selecting the multimodal biometric trait for considering the scenario is also challenging.
It measures the ratio of imposters are false accepted. If the threshold is high, low FAR is achieved. It is clear from
Sl. No. | Threshold | FAR | FRR |
---|---|---|---|
1 | 0.20 | 0.000 | 99.107 |
2 | 0.30 | 0.000 | 39.778 |
3 | 0.40 | 0.08 | 0.324 |
4 | 0.50 | 99.689 | 0.000 |
Sl. No. | Threshold | FAR | FRR |
---|---|---|---|
1 | 0.20 | 1.1 | 1.9 |
2 | 0.30 | 3.8 | 5.9 |
3 | 0.40 | 58.7 | 61.8 |
4 | 0.50 | 96.5 | 89.7 |
It is determined by the number of Genuines are falsely rejected. If the threshold falls low, FRR rate is high.
It is calculated by the formulae when FAR is equal to FRR. If the devices are accurate when ERR is low. Lower ERR indicates better performance. The proposed fusion algorithm is illustrated in
Both inner and outer boundaries are not concentric circles. Iris is a Physiological Biometrics. It is done by measuring the distance between pupil the boundary of Iris. Normalized hamming Distance is applied for matching module. It reduces error rates and improves the speed. It can be employed for high resolution or low resolution
Sl. No. | Threshold | FAR | FRR |
---|---|---|---|
1 | 0.20 | 4.54 | 5.7 |
2 | 0.30 | 7.90 | 6.9 |
3 | 0.40 | 55.6 | 63.5 |
4 | 0.50 | 73.8 | 65.9 |
Sl. No. | Threshold | FAR | FRR |
---|---|---|---|
1 | 0.20 | 12.9 | 7.0 |
2 | 0.30 | 36.9 | 16.9 |
3 | 0.40 | 69.4 | 36.8 |
4 | 0.50 | 85.8 | 88.8 |
Sl.No. | FAR | FRR | ||||
---|---|---|---|---|---|---|
Iris | FingerVein | Palmprint | Face | Integrated | ||
1 | 1.0 | 0.04 | 2.8 | 1.70 | 14.6 | 0.91 |
2 | 0.1 | 0.69 | 5.9 | 3.02 | 38.4 | 2.1 |
3 | 0.01 | 1.80 | 9.6 | 6.05 | 59.8 | 4.5 |
4 | 0.001 | 2.96 | 12.6 | 7.06 | 61.2 | 7.3 |
images. Low resolution images for civil and commercial applications. The preprocessing is used to set up coordinate’s alignment and segments the images for feature extraction. Preprocessing of Finger vein takes segmentation, enhancement, filtering and thinning. The median filter is used for image denoising. Thinning removes selected foreground pixels from binary code. Fusion image which is the combination of Iris, Finger Vein, Palm print and the Face. Integrated model produces a better FRR when compared with the traditional Iris, Finger Vein, Palm print and Face.
This paper presents a robust algorithm and secured at multiple levels, efficient by means of optimization technique to meet the performance needs of a multimodal biometric authentication system. Neural Network, Phase based techniques enhances performance and efficiency. Multimodal biometric eliminates demerits of unimodal biometric algorithms. The solution provided is best for authentication algorithm.
The future enhancement of this paper is that the other biometric traits can be considered as Brain and Heart Patterns, DNA, Aging Facial problems.
I would like to express my gratitude to the almighty god and visible god Parents who supports morally and my Husband Mr. D. Mohankumar for his motivation and my dear son M. S. Sanjay for his encouragement to pursue my Ph.D. degree.
E. Sujatha,A. Chilambuchelvan, (2016) Neural Network Based Normalized Fusion Approaches for Optimized Multimodal Biometric Authentication Algorithm. Circuits and Systems,07,1199-1206. doi: 10.4236/cs.2016.78103