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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.
... Most of the multimodal-based systems focused on improving the identification performance. The study in [102] proposed a multimodal biometric authentication system based on fingerprint and finger vein biometric traits. In this work, the palmprint and finger vein features were fused at score level using the normalization score of each trait, with a recognition rate of 98.5% was reported. ...
... The first problem was about the performance improvement of the multimodal systems. Several studies [94,[100][101][102] focus on improving the recognition accuracy of matching level fusion-based multimodal systems. The second problem was the poor performance of the system on noisy data. ...
... The third problem with such kind of multimodal system was about non-adaptiveness to a dynamic environment. Two works [102,105] were proposed to focus on this problem. The last problem was computational cost because of using different sensors for each modality and not the user-friendly nature of the acquisition device. ...
Finger vein recognition biometric trait is a significant biometric modality that is considered more secure, reliable, and emerging. This article presents a review to focus on the recent research landscape in biometric finger vein recognition systems. This article focuses on manuscripts related to keywords ‘Finger Vein Authentication System’, ‘Anti-spoofing or Presentation Attack Detection’, ‘Multimodal Biometric Finger Vein Authentication’ and their variations in four main digital research libraries such as IEEE Xplore, Springer, ACM, and Science Direct. The final set of articles is divided into three main categories: Deep Learning (DL) based finger vein recognition, Presentation Attack Detection (PAD), and Multimodal-based finger vein authentication system. Deep learning-based finger vein recognition techniques are further sub-divided into pre-processing (Quality assessment and enhancement) based, feature extraction based, and feature extraction and recognition based schemes. Presentation attack detection methods are sub-divided into software-based and hardware-based approaches. Multimodal-based finger vein biometric system is sub-categorized into feature level fusion, matching level fusion, and hybrid fusion methods. The authors have studied the problem of the recent algorithm and their solution related to finger vein biometric system from the recent literature. Performance analysis and selected the best promising research work from the mentioned studies are also presented. Finally, open challenges, opportunities, and suggested solutions related to deep learning, PAD, and Multimodal based finger vein recognition systems have been discussed in the discussion section. This work would be helpful to the developers, company users, researchers, and readers to get insight into the importance of such technology and the recent problem faced by finger vein authentication technology with respect to deep learning and multimodal systems.
Nowadays, the vein based recognition system becomes an emerging and facilitating biometric technology in the recognition system. Vein recognition exploits the different modalities such as finger, palm and hand image for the person identification. In this work, the fuzzy least brain storm optimization and Euclidean distance (EED) are proposed for the vein based recognition system. Initially, the input image is fed into the region of interest (ROI) extraction which obtains the appropriate image for the subsequent step. Then, features or vein pattern is extracted by the image enlightening, circular averaging filter and holoentropy based thresholding. After the features are obtained, the entropy based Euclidean distance is proposed to fuse the features by the score level fusion with the weight score value. Finally, the optimal matching score is computed iteratively by the newly developed fuzzy least brain storm optimization (FLBSO) algorithm. The novel algorithm is developed by the least mean square (LMS) algorithm and fuzzy brain storm optimization (FBSO). Thus, the experimental results are evaluated and the performance is compared with the existing systems using false acceptance rate (FAR), false rejection rate (FRR) and accuracy. The performance outcome of the proposed algorithm attains the higher accuracy of 89.9% which ensures the better recognition rate.
This paper presents a robust algorithm for ear identification based on geometrical features of the ear and Kohnen Self Organization Maps (SOM). Using ears in identifying people has been interesting at least 100 years. The researches still discuss if the ears are unique or unique enough to be used as biometrics. Ear
shape applications are not commonly used, yet, but the area is interesting especially in crime investigation. In this paper we present the basics of using ear as biometric for person identification and authentication. High resolution ear images are taken by high resolution digital camera. Six images have been
taken for twenty three persons. Four geometrical distances were calculated for each image. These geometrical distances are used as an input to the unsupervised Kohonen self organization maps. The accuracy of identification were found to be equal to 98%, for the proposed system .We conclude that that
the proposed model gives faster and more accurate identification of persons based on the ear biometrics and it works as promising tool for person identification of persons from the mage of their ear for criminal investigation purposes.
Finger vein authentication is a personal identification technology using finger vein images acquired by infrared imaging. It is one of the newest technologies in biometrics. Its main advantage over other biometrics is the low risk of forgery or theft, due to the fact that finger veins are not normally visible to others. Extracting finger vein patterns from infrared images is the most difficult part in finger vein authentication. Uneven illumination, varying tissues and bones, and changes in the physical conditions and the blood flow make the thickness and brightness of the same vein different in each acquisition. Accordingly, extracting finger veins at their accurate positions regardless of their thickness and brightness is necessary for accurate personal identification. For this purpose, we propose a new finger vein extraction method which is composed of gradient normalization, principal curvature calculation, and binarization. As local brightness variation has little effect on the curvature and as gradient normalization makes the curvature fairly uniform at vein pixels, our method effectively extracts finger vein patterns regardless of the vein thickness or brightness. In our experiment, the proposed method showed notable improvement as compared with the existing methods.
Due to the increase in security requirements, biometric systems have been commonly utilized in many recognition applications. Multimodal has great demands to overcome the issue involved in single trait system and it has become one of the most important research areas of pattern recognition. We present multimodal face and finger veins biometric verification system to improve the performance. We presented multilevel score fusion of face and finger veins to provide better accuracy. Simulation results shows that proposed multimodal recognition system is very efficient to reduce the false rejection rate.
This paper deals with a new approach for detection of finger veins. The method is split into four subsequent steps. The first of them consists of basic series of image filtering of the vein pattern and the other three are sequences of image filters for determination of the finger con-tour used for the background masking. At the end the finger veins with finger contour are ex-tracted and sent to the template generation unit.
Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. In this paper, (2D)² PCA is applied to extract features of finger veins, based on which a new recognition method is proposed in conjunction with metric learning. It learns a KNN classifier for each individual, which is different from the traditional methods where a fixed threshold is employed for all individuals. Besides, the SMOTE technology is adopted to solve the class-imbalance problem. Our experiments show that the proposed method is effective by achieving a recognition rate of 99.17%.
Palmprint recognition has attracted various researchers in recent years due to its richness in amount of features. In this work, palmprint authentication system is classified into palmprint acquisition, preprocessing, feature extraction, feature fusion and matching. In the preprocessing stage we employed a modified preprocessing technique to extract the ROI and it is further enhanced using adaptive histogram equalization. In feature extraction, the single sample representation has become bottleneck in producing high performance. To solve this we propose an intramodal feature fusion for palmprint authentication. The proposed system extracts multiplefeatures like Texture (Gabor), Line and Appearance (PCA) features from the preprocessed palmprint images. The feature vectors obtained from different approaches are in different dimensions and also the features from same image may be correlated. Therefore, we propose wavelet based fusion techniques to fuse extracted features as it contains wavelet extensions and uses mean-max fusion method to overcome the problem of feature fusion. Finally the feature vector is matched with stored template using NN classifier. The proposed approach is validated for their efficiency on PolyU palmprint database of 200 users. The experimental results illustrates thatthe feature level fusion improves the recognition accuracy significantly.
Palmprint recognition has attracted various researchers in recent years due to its richness in amount of features. In feature extraction, the single feature has become bottleneck in producing high performance. To solve this we propose an intramodal feature fusion for palmprint authentication. The proposed system extracts multiple features like Texture (Gabor), and Line features from the preprocessed palmprint images. The feature vectors obtained from different approaches are incompatible and also the features from same image may be redundant. Therefore, we propose a Particle Swarm Optimization (PSO) based technique to perform feature fusion on extracted features. Being an iterative technique that randomly optimizes the fused feature space, it overcomes the problems of feature fusion. Finally the feature vector is further reduced using Principal Component Analysis (PCA) and matched with stored template using NN classifier. The proposed approach is validated for their efficiency on PolyU palmprint database of 200 users. The experimental results illustrates that the feature level fusion improves the recognition accuracy significantly.
Biometrics systems for identification purposes have been developed for decades. Different methods include fingerprint, face, iris, retina, signature, gait, voice, hand vein, hand/finger geometry, DNA information have been proposed while fingerprint, face, iris and signature are considered as traditional identification methods. Each method has its disadvantages. Fingerprint systems usually have low security because they remain after touching a surface, hence patterns can be copied. Similarly, face and voice patterns can easily be cloned. Iris scanning reflects a light into eyes which make the system unfriendly. Contrasting with other biometrics, vein patterns makes the systems more secure and distinguishable because they are hidden inside the body and the situation of outer skin can not effect on that. This study investigated a Smart Access Control using Finger Vein authentication and Neural Network. Fourteen finger vein images collected from individuals by shining a near-infrared light through fingers. Automated image cropping was implemented. Image processing was done for reducing noise of finger vein images. The patterns of veins were extracted by combining two segmentation methods include: (i) Morphological Operation (ii) Maximum Curvature Points in Image Profiles. After extracting the vein image features, Neural Network was used to get the quality of training and testing. Neural Network was also applied for the purpose of recognizing individuals.
We propose a method of personal identification based on finger-vein patterns. An image of a finger captured under infrared light contains not only the vein pattern but also irregular shading produced by the various thicknesses of the finger bones and muscles. The proposed method extracts the finger-vein pattern from the unclear image by using line tracking that starts from various positions. Experimental results show that it achieves robust pattern extraction, and the equal error rate was 0.145% in personal identification.
Recently, biometric palmprint has received wide attention from researchers. It is well-known for several advantages such as stable line features, low-resolution imaging, low-cost capturing device, and user-friendly. In this paper, an automated scanner-based palmprint recognition system is proposed. The system automatically captures and aligns the palmprint images for further processing. Several linear subspace projection techniques have been tested and compared. In specific, we focus on principal component analysis (PCA), fisher discriminant analysis (FDA) and independent component analysis (ICA). In order to analyze the palmprint images in multi-resolution-multi-frequency representation, wavelet transformation is also adopted. The images are decomposed into different frequency subbands and the best performing subband is selected for further processing. Experimental result shows that application of FDA on wavelet subband is able to yield both FAR and FRR as low as 1.356 and 1.492% using our palmprint database.
In this paper, a novel method for palmprint recognition, called Fisherpalms, is proposed. In this method, each pixel of a palmprint image is considered as a coordinate in a high-dimensional image space. A linear projection based on Fisher’s linear discriminant is used to project palmprints from this high-dimensional original palmprint space to a significantly lower dimensional feature space (Fisherpalm space), in which the palmprints from the different palms can be discriminated much more efficiently. The relationship between the recognition accuracy and the resolution of the palmprint image is also investigated. The experimental results show that, in the proposed method, the palmprint images with resolution 32 × 32 are optimal for medium security biometric systems while those with resolution 64 × 64 are optimal for high security biometric systems. High accuracies (>99%) have been obtained by the proposed method and the speed of this method (responding time⩽0.4 s) is rapid enough for real-time palmprint recognition.
This paper presents a pattern recognition framework for face recognition based on the combination of Radon and discrete cosine transforms (DCT). The property of Radon transform to enhance the low frequency components, which are useful for face recognition, has been exploited to derive the effective facial features. Data compaction property of DCT yields lower-dimensional feature vector. The proposed technique computes Radon projections in different orientations and captures the directional features of the face images. Further, DCT applied on Radon projections provides frequency features. The technique is invariant to in-plane rotation (tilt) and robust to zero mean white noise. The proposed algorithm is evaluated using FERET and ORL databases. The experimental results show the superiority of the proposed method compared to some of the existing algorithms.
Abstract A new method,to extract vein patterns from an un- clear finger image is proposed.,Finger vein pattern can be used for personal identification technology. The finger vein image,that is captured,with infrared light contains,not only the vein patterns,but also irregu- lar shades,and noises. They result from the different thickness between finger bones and muscles and, light intensity fluctuations also produce them. Therefore, the robustness,and the tolerance to the uneven,lumi- nance,and noises in pattern extraction algorithm,are required. In this paper, we propose a method for extracting the finger vein patterns,in the image,that is unclear and/or affected by the fluctuations of LED intensity. The method,is based on special line tracking that starts at various positions. The robustness of the finger vein extraction method against LED intensity fluctuation is tested and the re- sult shows,the performance,of the method,is more,ro- bust than the conventional,method. In the evaluation of the personal,identification using the proposed,ex- traction method, the finger vein patterns of 678 people are collected. The performance,is 0.145% equal error rate in the evaluation.
In the finger-vein authentication, there are two problems in practice. One is that the quality of the vein image will be reduced under bad environment conditions; the other is the irregular distortion of the image caused by the variance of the finger poses. Both problems raise the error ratios. In this paper, we introduced a wide line detector for feature extraction, which can obtain precise width information of the vein and increase the information of the extracted feature from low quality image. We also developed a new pattern normalization model based on a hypothesis that the finger's cross-sections are approximately ellipses and the vein that can be imaged is close to the finger surface. It can effectively reduce the distortion caused by the pose. In our experiment based on a database containing 50,700 images, our method shows advantages on dealing with the low quality data collected from the practical personal authentication system.
The Multimodal Biometric based user authentication systems are highly secured and efficient to use and place total trust on the authentication server where biometric verification data are stored in a central database. Such systems are prone to dictionary attacks initiated at the server side. In this paper, we propose an efficient approach based on multimodal biometrics (Palmprint and Iris) based user authentication and key exchange system. In this system, texture properties are extracted from the palmprint and iris images are stored as encrypted binary template in the server’s database, to overcome the dictionary attacks mounted by the server. The image processing techniques are used to extract a biometric measurement from the palmprint and iris. During login procedure the mutual authentication is done between the server and user and a symmetric key is generated on both sides, which could be used for further secure communication between them. Thus meet-in-the middle attack that happens between the user and the server can also be overcome. This system can be directly applied to strengthen existing password or biometric based systems without requiring additional computation.
Analysis of different techniques for finger-vein feature extraction
- I Malik
- R Sharma
A robust algorithm for ear recognition system based on self organization maps. 1st Regional Conference of Engineering Science NUCEJ (special issue)
- A A Hussein
- Al-Timemy
Multimodal face and finger veins biometric authentication
- I R Muhammad
- IR Muhammad