# Multimodal Biometric Authentication System Based on Score-Level Fusion of Palmprint and Finger Vein

## Abstract

Multimodal biometrics plays a major role in our day-to-day life to meet the requirements with the well-grown population. In this paper, palmprint and finger vein images are fused using normalization scores of the individual traits. Palmprint features extracted from the discrete cosine transform (DCT) are classified by using multi-class linear discriminant analysis (LDA) and self-organizing maps (SOM). Finger vein identification is designed and developed by using repeated line tracking method to extract the patterns. A multimodal biometric authentication system integrates information from multiple biometric sources to compensate for the limitations in performance of each individual biometric system. These systems can significantly improve the recognition performance of a biometric system apart from catalyzing population coverage, impeding spoof attacks, increasing the degrees of freedom, and reducing the failure rates.

### Keywords

Multimodal biometrics DCT Multi-class LDA SOM## 1 Introduction

In recent years, multimodal biometrics has gained the substantial attention to all organizations and more than one biometric is fused together. Vein patterns serve a highly secured authentication system over other biometrics. It is noninvasive, reliable, and well accepted by users [1]. Preprocessing of finger vein images yields a better quality image by removing the noise and increasing the image contrast [2]. Acquisition of infrared finger vein image using various LED contains not only the vein patterns but also irregular shading produced by the various thicknesses of the finger bones and muscles [3]. The finger vein pattern from the unclear images is extracted by using line tracking, which starts from various positions. A person retrieval solution using finger vein can be accomplished by searching an image in the database in a reasonable time [4]. A wide line detector for feature extraction can obtain precise width information of the finger vein and improve the inferences of the extracted feature from low-quality image [5]. The finger vein patterns extracted by using gradient-based threshold and maximum curvature points are applied to neural network to train and test the quality of system [6, 7]. Extracting the finger vein patterns regardless of vein thickness or brightness is necessary for accurate personal identification [8]. Face and finger vein biometric authentication system at multi-level score-level fusion is very efficient to reduce the false rejection rate [9]. Multiple features like texture (gabor), line, and appearance (PCA) features extracted from the palmprint images are fused using particle swarm optimization techniques to improve the performance [10, 11, 12]. The wavelet-based fusion technique is suggested to fuse extracted features as it contains wavelet extensions and uses mean–max fusion method to overcome the problem of feature fusion [13].

The rest of this paper is organized as follows: Sect. 2 describes the palmprint recognition system. Section 3 highlights the feature extraction algorithm for finger vein authentication system. Score-level fusion is discussed in Sect. 4. Section 5 provides experimental results of the proposed system, and Sect. 6 offers the conclusion.

## 2 Palmprint Authentication

This paper designs the most efficient, high-speed method for palmprint recognition and also develops an algorithm for the palmprint recognition system which formulates an image-based approach, using the 2-dimensional discrete cosine transform (2D-DCT) for image compression and a combination of multi-class linear discriminant analysis (LDA) and self-organizing map (SOM) neural network [7] for recognition purpose.

### 2.1 Image Compression

The image compressed using 2D blocked discrete cosine transform (DCT) is applied with a mask, and high coefficients in the image are discarded.

*k*

_{1}= 0, 1, … ,

*N*

_{1}−1 and

*k*

_{2}= 0, 1, … ,

*N*

_{2}-1

For *n*_{1} = 0, 1… *N*_{1} − 1 and *n*_{2} = 0, 1… *N*_{2} − 1

### 2.2 Multi-class LDA

*i*,

*m*

_{i}is the number of cases, and the superscript T indicates a transpose action. The objective of FLDA is then to find \( {\text{U}}_{\text{opt}} \) maximizing the ratio of the between-class scatter to the within-class scatter.

### 2.3 Self-Organizing Feature Maps

Thus, units close to the winners as well as the winners themselves have their weights updated appreciably.

## 3 Finger Vein Authentication

The patterns of veins were extracted by combining two segmentation methods, which include morphological operation and maximum curvature points in image profiles. The finger vein patterns were acquired by passing near-infrared light through the finger vein. The result is an image of the unique patterns of veins, as dark lines can be captured by a sensor placed below the finger.

### 3.1 Feature Extraction and Matching

*x*

_{c}

*, y*

_{c}) is (

*x*

_{s}

*, y*

_{s}).

*R*

_{f}is the set of pixels within the finger’s outline, and

*T*

_{r}is the locus space.

*D*

_{lr}and

*D*

_{ud}are the parameters that prevent the tracking point and are determined by

*R*

_{nd}(

*n*) is uniform random number between 0 and

*n*

*N*

_{c.}

*N*

_{r}(

*x*

_{c}

*, y*

_{c}) is the set of neighboring pixels of (

*x*

_{c}

*, y*

_{c}), selected as follows:

*N*

_{3}(

*D*) (

*x, y*) is the set of three neighboring pixels of (

*x*

_{c}

*, y*

_{c}) whose direction is determined by the moving direction attribute

*D.*

*p*

_{lr}and

*p*

_{ud}are the probability of selecting the three neighboring pixels in the horizontal or vertical direction. The line evaluation function reflects the depth of the valleys in the cross-sectional profiles around the current tracking point:

*W*is the width of the profiles,

*r*is the distance between (

*x*

_{c}

*, y*

_{c}) and the cross section, and

*θ*

_{i}is the angle between the line segments (

*x*

_{c}

*, y*

_{c}) − (

*x*

_{c}+ 1

*, y*

_{c}) and (

*x*

_{c}

*, y*

_{c}) − (

*x*

_{i}

*, y*

_{i}). The current tracking point (

*x*

_{c}

*, y*

_{c}) is added to the locus position table

*T*

_{c}. The total number of times the pixel (

*x, y*) has been in the current tracking point in the repetitive line tracking operation is stored in the locus space,

*T*

_{r}(

*x, y*). Therefore, the finger vein pattern is obtained as chains of high values of

*T*

_{r}(

*x, y*). The patterns of veins shown in Fig. 2 are extracted using repeated lines tracking and by iterative process every minute details of finger vein are taken into account.

## 4 Score-Level Fusion

The matching scores of palmprint recognition is obtained by finding the minimum absolute deviation for each palmprint image. The matching scores of finger vein recognition are normalized using Z-score normalization technique. The mean (μ) and standard deviation (σ) are estimated from a given set of matching scores.

*s*

_{1}and

*s*

_{2}are palmprint matching score and finger vein matching score,

*w*

_{1}and

*w*

_{2}are the weights assigned to both the traits, and

*S*is the fusion score (Fig. 3).

## 5 Experimental Results and Discussions

Absolute minimum deviation of palmprint images

No. of subjects | Minimum deviation |
---|---|

1 | 4 |

2 | 8 |

3 | 13 |

4 | 17 |

5 | 23 |

Normalization scores of finger vein images

No. of subjects/samples | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|

1 | 74.5689 | 75.6421 | 75.2389 | 75.8999 | 74.1256 |

2 | 82.5671 | 82.9795 | 82.1256 | 82.0145 | 82.4789 |

3 | 86.2356 | 86.1005 | 86.0005 | 86.1856 | 86.1458 |

4 | 73.4566 | 73.0025 | 73.1255 | 73.0189 | 73.5809 |

5 | 90.1289 | 90.1478 | 90.1456 | 90.1236 | 90.1006 |

Recognition performance of the proposed system

Traits | FAR % | Recognition Rate % |
---|---|---|

Palmprint | 6 | 94.5 |

Finger vein | 4 | 96 |

Palmprint + Finger vein | 2 | 98.5 |

## 6 Conclusion

The proposed method bypasses the need to perform score normalization and choosing optimal combination weights for each modality. In this sense, the proposed solution is a principled and general approach that is optimal when the matching score distributions are either known or can be estimated with high accuracy. Palmprint authentication is implemented using LDA and SOM for feature classification and 2D-DCT for image compression. Finger vein is authenticated by using repeated line tracking for feature extraction, and matching is done with the template created. Once identification is done, minimum deviation for palmprint and matching score for finger vein are calculated and are fused using scoring level. Error rate is reduced, and it provides accurate results.

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