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%.
Finger vein recognition is a promising biometric recognition technology which verifies identities through finger vein patterns. Medical studies have shown that the finger vein pattern is unique and stable. In detail, the finger veins of an individual are different from the others’, and even the veins captured from a single individual are quite different from one finger to another. Furthermore, the finger veins are also invariant for healthy adults.
Compared with fingerprints, finger veins are hard to be forged or stolen as they are hidden inside the fingers. The contactless captures of finger veins also ensure both convenience and cleanliness, and they are user-friendly. Furthermore, Finger veins are less affected by physiology and environment factors such as dry skin and dirt.
A typical finger vein recognition process is composed of the following four steps. Firstly, the finger vein images are obtained via the finger vein capturing devices. Secondly, the finger vein images are preprocessed. Thirdly, the features are extracted. Finally, the finger vein images are matched based on the extracted features.
The preprocessing procedure includes image enhancement, normalization, and segmentation. For image enhancement, Yang and Yan incorporated directional decomposition and Frangi filtering to enhance the image quality [
Methods for personal authentication using finger vein recognition.
References | Method | Database fingers × samples per each | Performance |
---|---|---|---|
[ | Linetracking | EER: 0.145% | |
[ | Mean curvature | EER: 0.25% | |
[ | Wide line detector | EER: 0.87% | |
[ | Statistical vein energy | CCR: 98.7% | |
[ | Moment invariants | EER: 8.93% | |
[ | Sliding window matching | EER: 0.54% | |
[ | Manifold learning | EER: 0.8% | |
[ | PCA + BP network | CCR: 99% | |
[ | PCA + LDA + SVM | CCR: 98% |
PCA is a popular linear dimensionality reduction and feature extraction technology. It has extensive applications in image processing. Wu and Liu extracted the PCA features and then trained a neural network for matching, which results in a high recognition rate [
Recently, more and more researchers apply machine learning methods to finger vein recognition. Liu et al. introduced manifold learning to finger vein recognition [
There are two challenges for finger vein recognition: (1) how to efficiently extract distinguishing features and (2) how to design a strong classifier with high recognition rate and fast recognition speed to make the system more practical in real-world applications.
To overcome these two challenges, in this paper we apply (2D)2 PCA to extract the features from finger vein images. In order to address the shortcoming of traditional distance-metric-based classifiers, we build a classifier for each individual based on metric learning. With regard to training samples of each classifier, the number of positive samples is inadequate as compared to the negative samples. Thus, we use SMOTE technology to oversample the positive samples to balance the two classes before training the classifier. The experimental results show that the proposed method has good performance on finger vein recognition.
The rest of this paper is organized as follows. The technical background is briefly introduced in Section
PCA is a typical linear dimensionality reduction and feature extraction method. Due to the transformation from the 2-dimensional image matrix into a 1-dimensional column vector, PCA often makes the size of the corresponding covariance matrix too large, and computing the eigenvectors and eigenvalues becomes time-consuming. In order to solve this problem, Yang et al. proposed 2DPCA to extract the features [
Considering
For a random image matrix
So,
It has been proven that
Because
Let the image matrix
Similarly, to achieve the projected matrix
Using the projected matrix
We can see from (
Most machine learning methods use distance metrics to measure the dissimilarity of instances. Metric learning is able to learn an appropriate distance metric. The main task of the metric learning is to find a better distance metric, based on which the distances between the samples from same class become small while those from different classes become large. This helps to improve the performance of the machine learning methods.
To overcome the shortage of the KNN classifier using Euclidean distance, Weinberge et al. proposed a metric learning method called LMNN (Large Margin Nearest Neighbor) [
Let
An example of LMNN.
In Figure
The performance of machine learning algorithms is typically evaluated by prediction accuracy. However, this is not applicable when the data is imbalanced. Existing solutions to the class imbalance problem can be divided into two categories. One is to assign distinct costs to training examples. The other is to resample the original dataset, either by oversampling the minority class and/or undersampling the majority class.
Chawla et al. proposed an oversampling approach called SMOTE where the minority class is oversampled by creating “synthetic” examples [
The proposed method includes training process and recognition process. As shown in Figure
The proposed framework for finger vein recognition.
In the training process, it is necessary to preprocess the infrared images of the finger veins. Preprocessing includes grayscale, ROI selection, and normalization (e.g., size normalization and gray normalization). After the preprocessing, we apply (2D)2 PCA to extract the features of the training samples. Then we label the samples as positive and negative class accordingly and oversample the positive samples with SMOTE. We learn a new distance metric, that is, the transformation matrix
The preprocessing and feature extraction in the recognition process are similar to that in the training process. After that, we input the features of the samples to train classifier to verify the individual based on the classification result.
The preprocessing includes image grayscale, ROI selection, size normalization, and gray normalization.
The original image (an example is shown in Figure
Examples of preprocessing.
As the background of finger vein region might include noise, we employ an edge-detection method to segment the finger vein region from the gray-scale image. A Sobel operator with a
The size of the selected ROI is different from image to image due to personal factors such as different finger size and changing location. Therefore it is necessary to normalize the ROI region to the same size before the feature extraction process by (2D)2 PCA. We use bilinear interpolation for size normalization in this paper, and the size of the normalized ROI is set to be
In order to extract efficient features, gray normalization is used to obtain a uniform gray distribution (as shown in Figure
where
After the preprocessing, we extract the features for each image by (2D)2 PCA and assign labels for them. A classifier is trained for every individual, where the samples belonging to this individual are treated as positive and others are negative. We oversample the positive samples by SMOTE to obtain an augmented training set which achieves class balance in general. LMNN is then used on the augmented training set to obtain a transformation matrix
In the verification mode, we input the feature vector of a test sample to a classifier which represents a certain individual, and then we verify whether the sample belongs to this individual based on the classification result. In the identification mode, we employ all classifiers to classify the test sample. If only a classifier C classifies it as positive class, this sample belongs to the individual which corresponds to the classifier C. If there are many classifiers classifying the sample as positive class, then we use the training accuracy rate for decision making: the sample belongs to the individual that corresponds to the classifier with the best training accuracy.
The experiments were conducted using our finger vein database which is collected from 80 individuals’ (including 64 males and 16 females, Asian race) index fingers of right hand, where each index finger contributes 18 finger vein images. Each individual participated in two sessions, separated by two weeks (14 days). The age of the participants was between 19 and 60 years, and their occupations included university students, professors, and workers at our school. The capture device was manufactured by the Joint Lab for Intelligent Computing and Intelligent System of Wuhan University, China, which is illustrated in Figure
The finger vein capture device.
The original spatial resolution of the data is
Sample finger vein images.
All the experiments are implemented with MATLAB and conducted on a machine with 2.4 G CPU and 4 G memory.
We design three experiments to verify the efficiency of the proposed method. In Experiment
In this experiment, we first generate four data sets as follows. We select 480, 720, 960, and 1200 images (i.e., 6, 9, 12, and 15 images for each individual) for training, and the rest of 960, 720, 480, and 240 images (i.e., 12, 9, 6, and 3 images for each individual) are left for testing, respectively. The Euclidean-distance-based recognition method works in the following way. We treat the training samples from each individual as the positive class and construct a center point for each class, where the
The metric-learning-based method works similarly as the Euclidean-distance-based method except for the usage of the learned distance metric
It is clearly seen that the recognition rate of the metric-learning-based method is higher than the Euclidean-distance-based method. With distance metric transformation, two samples from different classes with small Euclidean distance are dragged farther. On the other hand, two samples from the same class with large Euclidean distance are pulled closer. Furthermore, the samples from different classes are separated by a large margin. Next we are going to provide an intuitive explanation based on the example shown in Figures
These two figures show the data distribution of the data set with 480 training samples and 960 testing samples. We obtain 25 features for each sample using (2D)2 PCA and select 2 features with the largest contribution to Euclidean distance metric. These two features constitute the vertical and horizontal coordinates of Figure
The recognition rates of the compared methods.
Euclidean-distance-based method | Metric-learning-based method | |
---|---|---|
480 training, 960 testing | 78.96% | 86.46% |
720 training, 720 testing | 82.08% | 91.25% |
960 training, 480 testing | 86.25% | 92.29% |
1200 training, 240 testing | 84.58% | 93.75% |
Samples distribution with Euclidean distance metric.
Samples distribution with new distance metric using LMNN.
In this experiment, we select 6, 9, 12, and 15 images from each individual as training samples to build a KNN classifier. The underlying distance metric for each individual is learned by LMNN. Here the number of neighbors, that is,
Overall, the recognition rate increases with the number of training images increases. When the number of the training images goes to 15, the recognition rate reaches 96.67%. It is also worth noting that, as compared to Table
The recognition rates with different numbers of training images.
This experiment verifies that SMOTE can improve the classification performance. We select 1200 images (15 images for each individual) for training and 240 images (3 images for each individual) for testing. We use SMOTE to oversample the positive samples to be 5, 10, 20, 30, 40, and 50 times as large as the original set. The recognition result is shown in Table
The recognition rate by SMOTE.
Without SMOTE | 96.67% |
SMOTE-5 | 96.67% |
SMOTE-10 | 96.67% |
SMOTE-20 | 98.75% |
SMOTE-30 | 98.33% |
SMOTE-40 | 99.17% |
SMOTE-50 | 99.17% |
This paper proposes a new finger vein recognition method based on (2D)2 PCA and metric learning. Firstly, we extract features by (2D)2 PCA and then train a binary classifier for each individual based on metric learning. Furthermore, we address the class imbalance problem by using SMOTE oversampling before the classifier is trained. The experimental results show that the proposed method achieves a recognition rate of 99.17%. The contributions of this paper are as follows. (1) We apply (2D)2 PCA to extract features of finger vein image, where (2D)2 PCA reflects the information in both the row direction and the column direction, and it is more efficient for feature extraction as compared to PCA and 2DPCA. (2) We build the KNN classifier based on metric learning using LMNN which changes the sample distribution in the new metric space. LMNN makes the distance between the samples from the same class smaller and the distance between the samples from different classes larger. Furthermore, we also employ a maximum margin framework to improve the recognition performance. This is incorporated with individually trained classifiers which reflect the characteristics of the corresponding individuals. (3) We note the class-imbalance problem; that is, when building the classifier for an individual, the number of the samples from the other individuals is considerably large. We tackle it by oversampling the positive samples with SMOTE, and the experimental results validate the effectiveness. Promising future work includes the exploration of features with better discrimination as well as the processing finger vein images of low quality.
This work is supported by National Natural Science Foundation of China under Grant nos. 61173069 and 61070097, and the Research Fund for the Doctoral Program of Higher Education under Grant no. 20100131110021. The authors would like to thank Shuaiqiang Wang and Guang-Tong Zhou for their helpful comments and constructive advice on structuring the paper. In addition, the authors would particularly like to thank the anonymous reviewers for their helpful suggestions.