Automated biometric authentication attracts the attention of researchers to work on hand-based images to develop applications in forensics science. Finger Knuckle Print (FKP) is one of the hand-based biometrics used in the recognition of an individual. FKP is rich in texture, less in contact and known for its unique features. The dimensionality of the features, extracted from the image, is one of the main problems in pattern recognition. Since selecting the relevant features is an important but challenging task, the feature subset selection is an optimization problem. A reduced number of features results in enhanced classification accuracy. The proposed FKP system presents a mulitalgorithm fusion based on subspace algorithms at feature level fusion technique. In this paper, a new feature-selection algorithm, which is a Modified Magnetotatic bacterium Optimization Algorithm (MMBOA), is proposed for finger knuckle recognition to select relevant and useful features that increase the classification accuracy. The distinct characteristic of this bacterium influences the design of a new optimization technique. The hybrid features such as
Authenticating a reliable user is important for e-commerce applications. In today’s real-life application, the world is afraid of the Coronavirus, which moves our biometric recognition system towards the contactless user identification system. Out of the various hand-based biometrics, Finger Knuckle print (FKP) is unique for an individual. Finger Knuckle represents the dorsum back surface of the hand. The texture and geometric shape features of the finger knuckle are used in the identification process of an individual to give the projected results. The advantage of using finger knuckle instead of other biometrics is its user-friendliness, invariant to emotions, low cost and user acceptance rate, which is incredibly high. The convex form lines and skin wrinkles on the finger dorsal surface are small in size, making them very unique in individual identification, which is typically not smashed as it is with the upper hand. Furthermore, the acquisition techniques require very little user interaction, resulting in high-speed recognition with low-resolution image cameras. As a result, using finger knuckle biometrics for identification will provide a distinct advantage in the field of physical biometrics. Woodard et al. [
Ajay Kumar proposed a FKP recognition model that uses finger knuckle lines and creases are highly rich in texture, local orientation features, which are extracted using radon transform performs better results when compared with Gabor, and Eigen knuckles [
Zhang et al. proposed a feature extraction scheme for FKP recognition. The classification of the image pattern is based on the feature extraction scheme and plays a key role in matching images and ROI’s accuracy. The local features such as local phase and local concurrency are integrated with the global features to enhance accuracy [
Feature Selection is a predictive model to select informative features and is considered as an input of the classification model. The dataset is large then it results to high dimensional data. Due to this phenomenon, the classification model is affected with negative impact in accuracy and computation cost [
Literature surveys show that many works have been done on Finger Knuckle recognition in feature extraction stage. However, Feature Selection is not much analyzed for finger knuckle recognition. For the first time, the MBOA optimization technique has been utilized for feature selection in FKP to provide promising results with reduced number of features. So far, the MBOA optimization technique is evaluated for the benchmarked dataset and this provides promising results. Salvatore Bellini, in 1963, identified the polyphyletic group of bacteria that move across the magnetic field line. The bacteria move towards an oxygen-concentrated region and the movement is performed with the help of magnetic crystals with magnetisms. It contains the fixed magnets, which align the bacteria moving towards the North Pole [
This paper is organized as follows; Section 2 presents the feature selection of the proposed optimization technique to select the discriminative features and feature extraction techniques. Section 3 provides the Experimental results and discussion of the proposed system, the Comparison of proposed feature selection with GWO method and also shows the comparison of proposed with other state of approaches and finally with conclusion in Section 4.
Finger knuckle recognition is based on the MMBOA_mr feature selection technique. Finger knuckle biometric exploits a new approach of choosing the optimal feature subset based on MMBOA_mr. The features are extracted based on the PCA and LDA combination and these feature vectors are fed to MMBOA_mr feature Selection.
The searching process is done iteratively to obtain the best subset features. It is based on the fitness function in terms of classification accuracy to validate the subset of features. The classification accuracy is taken as fitness value and is able to select the new feature subset. The bacteria contain multi cells and each cell contains the magnetosomes which is solution vector and the values of the vector are known for moment of the cells. The moment generation, moment regulation and moment replace are the factors which influences the feature reduction. Since the irrelevant features are eliminated the complexity of the system reduces the computational time and the search space.
The contributions of this work include: Finger Knuckle recognition is based on a new feature selection algorithm MMBOA_mr Ei and Fi features are extracted from the FKP image using PCA and LDA feature extraction techniques The MMBOA_mr feature selection technique is proposed for FKP recognition Performance of the proposed FKP recognition id evaluated using the performance metrics such as FAR, FRR, ERR and Accuracy.
Features extraction is the transformation of the original features to lower dimensionality with reduced number of features. The first step is to extract the features from the image. This research employs an appearance-based algorithm such as Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Both the PCA and LDA are linear transformations that reduce the computational cost and processing time.
In this paper, the combination Eigen and Fisher knuckle features are proposed to extract the features and it is known as EiFi features. The PCA and LDA are applied to the cropped images to extract the features.
LDA describes the vectors in the classes by constructing the d-dimensional mean vector. Then it finds the scatter matrix within and between the classes. The next sort the Eigen vectors according to the Eigen value and largest Eigen value forms the matrix d × k, where each column represents the eigenvector. Based on the d × k, the sample is transformed into a new subspace [
The transformation depends on the number of classes (c), number of samples (s) and dimensionality d. The main aim of LDA is to maximize the measure between classes and minimize the measure within classes [
PCA identifies the subspace, in which the optimal solution lies. The size of the pixel is reduced to minimal without the loss of information. In this work, the feature vector of the finger knuckle image is the Eigen vectors of the covariance matrix (Q). The features extracted from the knuckle image contain n × m pixel. Before computing the covariance matrix, the vectors are normalized to make the system invariant to be illuminated. The covariance matrix is too large to compute Eigen vector. Various authors discussed different results while eliminating the first three Eigen vectors. Face recognition achieves better performance [
Feature Selection plays an important role in selecting the subset of features by eliminating the irrelevant and unnecessary features. Classification accuracy depends on the best subset of learning features [
MBOA is a magnetotactic bacteria optimization algorithm that is also a new bio inspired algorithm. The Magnetotactic bacteria represent a group of prokaryotes occurring in the natural seawater and fresh water. The magnetic lines, which are known as magnetosomes create the moment to find the optimal solution in their environment. Magnetosomes consist of magnetite colloids and mineral particles arranged narrowly in the geomagnetic field direction. The moment is based on the energy level of each cell and the chemical signals around the environment. The biological characteristics of the bacteria are that they organize themselves and adjust automatically to move along the earth’s magnetic field. The behavior of the bacteria is to find the best optimal oxygen-concentration and redox potentials in the water [
For survival, the magnetic lines in the magnetosomes that will bend to reduce the magnetostatic energy. The magnetosomes produce the moments, which support the minimization of energy. The optimization of minimized computational cost and storage achieves better performance in the recognition of finger knuckle biometric.
Here, the parameters are tuned with the number of iterations. The performance of the proposed algorithm varies according to the parameter tuning. Here, 30 iterations of 100 different generations, with the population size of 50, is performed and the results are
MMBOA_mr and GWO | Experimental values |
---|---|
Maximum no of iterations | 3000 |
No of runs | 30 |
Initial population size | 50 |
MMBOA_mr | C1 = 1.5, C2 = 0.5 where, C1, C2 are constants, |
GWO | No of wolves = 4 |
The parameter setting includes the values such as C1 = 30, C2 = 0.004, mp = 0.7 and B = 0.1 and are selected as best for the benchmark functions [
The combination of PCA and LDA feature extraction techniques are used to extract the features using the optimization technique to select the relevant features. Finally, to analyze the accuracy, the selected features are fed to the KNN classifier. The proposed FKP recognition architecture is displayed in
The similitude between the Original MBOA and feature selection MMOBA_mr is the non-multi cells in the bacteria population where each cell (vector) is considered a feasible solution and the feature values are magnetosomes and moment of magnetosomes. Finally, the state of low concentration of magnetostatic energy is an obtained optimal solution
The population P. randomly generates the feature vector (cell). The cell is generated using
where i = 1, 2, 3 …. P (P is the size of the population), j = 1, 2, 3 … n, (dimension of the cell), max and mini are upper and lower bounds for the dimension j, rand (0, 1) is a random number from the uniform distribution (0, 1).
The distance between two cells is calculated as the interaction energy between the cells. The distance between the two cells are
Then P
The obtained Interaction distance
where ‘t’ is the current generation,
Moment’s generation is produced using an interaction energy
In MBOA_mr, the magnetic field B is the constant value 1. Then, the moment generation will be
The total moments can be regulated as follows:
Here, l
The regular MBOA is not following the regulation setup for the moments. The proposed MMBOA_mr evaluates the population of the cells whereby the aspect of fitness value is based on the current generation (t) classification accuracy and regulates the moment as
If
Otherwise
The worst features are omitted and the best features for each generation are included and calculated as per the fitness value. Some of the cell moments regulate the other cells, which improve the exploitation search (local minimum) as per
After the moment migration, the population is evaluated according to the cell’s fitness. Based on the cost, the solutions are sorted in ascending order. The replacement probability is set as 0.5. The worst features are ignored using the formula below.
The remaining moments are replaced by the
Initialize the random value as best value (Xbest)
Initialize the magnetic bacteria and memory as zero (X, F)
Initialize the population randomly (P) in search space
Initialize Parameters
Until stop criteria met to do
Calculate the fitness of each cell
Normalize the fitness
// Interaction distance
Calculate the Interaction Distance D from
If D > r
Calculate the Moment interaction Energy of the two cells from
Else
E = rand (1, P)* R
End if
End for
Obtain moment generation from
Evaluate the population according to the fitness
Visualize the moments and next the regulation of the moment follows
Calculate the moment M of each cell at t generation from
Regulate the moment of each cell at t generation
Evaluate the population according to the fitness
For each cell do
According to cost, the solution is sorted in ascending order
MTS replacement as in
End for
Archives best solution
The K-Nearest Neighbor (KNN) is a simple classifier. This supervised learning algorithm selects the minimum distance between the query samples and the trained samples. It is easy to implement using the K (K=1) value that defines the nearer number of neighbors. In this proposed work, KNN is used to ensure classification accuracy for the finger knuckle Recognition [
The performance of the proposed algorithm MMBOA_rm is discussed and presented. The implementation and testing are done using the poly u dataset. The Poly u finger knuckle dataset is used to prove the efficient recognition of an individual. The dataset with two sets: 70% for training and 30% for testing is used. The dataset has five images for the single user and is further divided into two sets for training and testing. For training 3 and for testing 2 images are given.
Feature Selection with stopping criteria for optimization is considered, a maximum of 30 generations with 100 iterations for each generation. It is done to prove the statistical implication and stability of the outcomes. After selecting the subsets of feature set, it is valued using the test set. This evaluation is done with the KNN classifier. The experiments are conducted with respect to different parameters and finally the adoptable results are discussed in below. Implementation is done in Matlab (2016) core™ i3-7100U, x-64 based processor, 64-bit operating system, and 4GB RAM. Based on the evaluation criteria, the different classifiers are implemented and compared to show the one that achieves better performance in the proposed work. The performance of the FKP recognition is generated with the metrics such as accuracy, FAR, FAR, EER
The FKP image features are extracted using PCA and LDA. It is represented as Eigen and fisher feature vectors. These two machine learning algorithms are used in feature extraction based on feature selection in various biometrics such as face, palm print, ear, finger vein [
The KNN classifier is used to classify the genuine users due to its simplicity. In
The proposed algorithm is compared with the Gray Wolf Optimizer (GWO). For Finger Knuckle recognition, the optimization algorithms for feature selection are used in limited study. Therefore, the comparison the Grey wolf Optimizer is implemented and the results compared with the proposed MBOA. Both the optimization algorithms such as MBOA and GWO show better results for the introduction of features. When GWO is compared with MBOA, MBOA outperforms well with minimum number of features and computational speed based on classification accuracy.
Performance comparison is based on these two algorithms:
Feature extraction | Total features | Optimized features | Performance metrics (%) | |||
---|---|---|---|---|---|---|
FAR | FRR | ERR | Accuracy | |||
Ei features (PCA) | 80 | 80 | 8.02 | 8.67 | 8.3 | 91.6 |
Fi features (LDA) | 80 | 80 | 6.09 | 7.07 | 6.58 | 93.42 |
EiFi features (PCA + LDA) | 80 | 80 | 1.02 | 1.4 | 1.21 | 98.6 |
EiFi features with proposed GWO | 80 | 34 | 0.43 | 0.58 | 0.505 | 99.49 |
EiFi features with proposed MMBOA_mr | 80 | 22 | 0.28 | 0.32 | 0.3 | 99.7 |
No of generations | Input Eifi features (A) | Reduced no of features (A-B) | Optimized selected features (B) | Accuracy (%) | |||
---|---|---|---|---|---|---|---|
GWO | MMBOA_mr | GWO | MMBOA_mr | GWO | MMBOA_mr | ||
10 | 80 | 71 | 63 | 9 | 17 | 90 | 93.2 |
15 | 80 | 62 | 63 | 18 | 17 | 95.3 | 97.6 |
20 | 80 | 62 | 61 | 18 | 19 | 98.8 | 99.4 |
25 | 80 | 51 | 63 | 29 | 17 | 98.7 | 99.6 |
30 | 80 | 46 | 58 | 34 | 22 | 99.4 | 99.7 |
No of users | Total Eifi features | Reduced no of features (A-B) | Optimized selected features (B) | Timing (s) | |||
---|---|---|---|---|---|---|---|
MBOA_mr | GWO | MBOA_mr | GWO | MBOA_mr | GWO | ||
10 | 20 | 1 | 1 | 19 | 19 | 130 | 140 |
15 | 30 | 1 | 1 | 29 | 29 | 150 | 160 |
20 | 40 | 1 | 1 | 39 | 39 | 210 | 270 |
25 | 50 | 3 | 2 | 47 | 48 | 270 | 290 |
30 | 60 | 1 | 1 | 59 | 59 | 330 | 340 |
35 | 70 | 4 | 3 | 66 | 67 | 402 | 450 |
40 | 80 | 58 | 46 | 22 | 34 | 420 | 550 |
Feature selection in various biometrics such as the hand based, palm print, ear and face [
In
Reference | Feature selection technique | No of features | Reduced no of features | Accuracy | Timing (S) |
---|---|---|---|---|---|
[ |
PSO | 322 | 159 | 96.8 | .05/image |
[ |
GA | 3200 | 800 | 87.2 | - |
[ |
ACO | 168 | 30 | 99.75 | 960 |
[ |
Fast correlation-based filter (FCBF) | 121 | 50 | 98.23 | - |
Sparse bayesian multinomial logistic regression (SBMLR) | 48 | 99.02 | |||
Spectrum feature selection algorithm | 50 | 97.31 | |||
[ |
GA | 403 | 25 | 97.51 | - |
In this paper | GWO | 80 | 34 | 99.4 | 550 |
In this paper | MMBOA_mr | 80 | 22 | 99.7 | 420 |
References | EER | Accuracy |
---|---|---|
[ |
3.94 | - |
[ |
0.22 | - |
[ |
5.95 | 88.27 |
[ |
0.78 | 99.24 |
[ |
3.97 | 90.52 |
[ |
1.59 | 95.43 |
GWO | 0.505 | 99.49 |
MMBOA_mr | 0.3 | 99.7 |
To evaluate the significant performance of the proposed algorithm, statistical test is done. Here, the MMBOA_mr and GWO algorithms are implemented with 30 runs. Here, the hypothesis test, t-test paired using two samples, is applied on the datasets that results 95% confidence level. The hypothesis test condition is depending on the
Mean | 0.982906 | 0.970137 |
Variance | 8.31E-05 | 0.000554 |
Observations | 30 | 30 |
Pearson correlation | −0.32781 | |
Hypothesized mean difference | 0 | |
df | 29 | |
t stat | 2.507503 | |
P(T <= t) one-tail | 0.009005 | |
t critical one-tail | 1.699127 | |
P(T <= t) two-tail | 0.01801 | |
t critical two-tail | 2.04523 |
For FKP recognition, this paper develops a novel feature-selection algorithm called Modified Magnetotatic Bacteria Optimization. The proposed FKP recognition extract features using hybrid EiFi feature extraction technique and Modified Magnetotatic Bacteria Optimization algorithm (MMBOA) for feature selection. MMBOA is able to provide the optimal subset of features for finger knuckle recognition that takes the least amount of time to compute and improves classification accuracy. MMBOA-KNN outperforms GWO-KNN in terms of accuracy and number of reduced features. Extensive experimental results and discussions indicate that our proposed methodology achieves significant enhancements than various existing finger Knuckle recognition algorithms. As demonstrated in the experiments, the proposed FKP recognition performs better and more efficiently than other state-of-the-art approaches, with higher accuracy of 99.7% and minimum EER of 0.3%.