1. Introduction
Biometrics has become more and more important solutions to overcome vulnerabilities of the security systems for people, companies, corporations, institutions and governments. Person identification systems based on biometrics were used in primarily limited applications requiring high security tasks like criminal identification and police work in the beginning, more recently they have been used in a wide range of applications including information security, law enforcement, surveillance, forensics, smart cards, access control,
etc. because of their reliability, performance and accuracy of identification and verification processes [
1–
4]. When the biometric literature was reviewed, it was found that there was extensive literature on fingerprint identification and face recognition. The researchers were mostly focused on designing more secure, hybrid, robust and fast systems with high accuracy by developing more effective and efficient techniques, architectures, approaches, sensors and algorithms or their hybrid combinations [
1,
2].
Generating a biometric feature from another is a challenging research topic. Generating face characteristics from only fingerprints is an especially interesting and attractive idea for applications. It is thought that this might be used in many security applications. This challenging topic of generating face parts from only fingerprints has been recently introduced for the first time by the authors in series of papers [
5–
13]. The relationships among biometric features of the faces and fingerprints (Fs&Fs) were experimentally shown in various studies covering the generation of:
face contours, including face border and ears [
6],
face models, including eyebrows, eyes and mouth [
7],
inner face masks including eyes, nose and mouth [
8],
face parts, including eyes, nose, mouth and ears [
9],
face models including eyes, nose, mouth, ears and face border [
10],
face parts, including eyebrows, eyes, nose, mouth and ears [
11],
face parts, including eyebrows, eyes and nose [
13],
face features, including eyes, nose and mouth [
14] and
face shapes, including eyes, mouth and face border [
15].
In these studies, face parts are predicted from only fingerprints without any need of face information or images. The studies have experimentally demonstrated that there are close relationships among faces and fingerprints.
Although various feature sets of faces and fingerprints, different parameter settings and reference points were used to achieve the tasks with high accuracy from only fingerprints, obtaining the face parts including the inner face parts with eyebrows and face borders with ears has not been studied up to now. In order to achieve the generation task automatically with high accuracy, a complete system was developed. This system combines all the other recent studies introduced in the literature and provides more complex and specific solutions for generating whole face features from fingerprints. In order to improve the performance of the proposed study, Taguchi experimental design technique was also used to determine best parameters of artificial neural network (ANN) models used in this generation. In order to evaluate and demonstrate the results more precisely, 10-fold cross validation technique with both quantitative (objective) evaluation metrics and expanded qualitative (subjective) evaluation metrics were used. So the performance and accuracy were demonstrated in a more reliable way with a limited database in comparison to the previous studies.
The paper is organized as follows. Section 2 reviews the background information on biometrics, automatic fingerprint identification and verification systems (AFIVSs), and face recognition systems (FRSs). Section 3 briefly introduces ANNs. Section 4 presents the motivations of this study as well as investigates the previous works about relationships among fingerprints and faces. Section 5 describes the evaluation methods. Section 6 presents the novelty of the proposed system including basic notations, definitions and various steps of the present method, the intelligent biometric feature prediction system (IBFPS). The experiments including numerical and graphical results of IBFPS are depicted in Section 7. Finally, the proposed work is concluded and discussed in Section 8.
2. Background of Biometric Systems
Biometric features covering physical or behavioral characteristics including fingerprint, face, ear, hand geometry, voice, retina, iris recognition,
etc. are
peculiar to the individual,
reliable as far as not being transferable easily and
invariant during the life time [
1]. Typical biometric systems include enrollment, identification, verification, recognition, screening or classification processes. The steps in system tasks are as follows: biometric data acquisition, feature extraction, registration, matching, making decision and evaluation. Biometric data were obtained from people with the help of a camera-like-device for the faces and fingerprint scanner for the fingerprints,
etc. In general, after data acquisition processes, the digital representation of the biometric data of the people were obtained in the digital platform. Feature extraction processes were applied to this digital form of the biometric features and feature sets were registered to the biometric system database. When a user wants to authenticate him/her self to the system, a fresh biometric feature was acquired, the same feature extraction algorithm is applied, and the extracted feature set is compared to the template in the database. If these feature sets of the input and the template biometric features are sufficiently similar according to the matching criteria, the user’s final decision was taken and the user was authenticated at the end of the matching process [
3,
14].
Data acquisition, verification, identification and screening phases are the main types of biometric based systems [
4]. The types are summarized as:
A typical biometric system is given in
Figure 1. The processes in the system are achieved according to the arrows illustrated in the figure depending on the application status.
These sort of biometric recognition systems make people, systems or information safer by reducing the fraud and leading to user convenience [
4]. Two of most popular biometric features used in the biometric based authentication systems are fingerprints and faces. Fingerprints based biometric systems are called AFIVSs and faces based biometric systems are called FRSs.
Fingerprints are unique patterns on the surface of the fingers. Fingerprints represent the people with high accuracy because of having natural identity throughout the life of which are not forgotten anywhere or not be lost easily. They were reliably and widely used to identify the people for a century due to its uniqueness, immutability and reliability [
17].
In AFIVSs, ridge-valley structure of the fingerprint pattern, core and delta points called singular points, end points and bifurcations called minutiaes are used for identifying an individual. These structures are given in
Figure 2. Many approaches to AFIVSs have been presented in the literature [
1,
2,
15,
17–
30]. The AFIVSs might be broadly classified as being
minutiae-based, correlation-based and
image-based systems [
18]. A good survey about these systems was given in the reference [
1].
The minutiae-based approaches rely on the comparisons for similarities and differences of the local ridge attributes and their relationships to make a personal identification [
19–
21]. They attempt to align two sets of minutiae from two fingerprints and count the total number of matched minutiae [
4]. If a minutiae and its parameters are computed relative to the singular points which are highly stable, rotation, translation and scale invariant, the minutiae will then become rotational, translational and scale invariant [
15,
22–
24]. Core points are the points where the innermost ridge loops are at their steepest. Delta points are the points from which three patterns deviate [
23,
25,
26]. The general methods to detect the singular points are Poincare-based [
27], intersection-based [
23] or filter-based [
28] methods.
Main steps of the operations in the minutiae-based AFIVSs are summarized as: selecting the image area; detecting the singular points; enhancing, improving and thinning the fingerprint image; extracting the minutiae points and calculating their parameters; eliminating the false minutiae sets; properly representing the fingerprint images with their feature sets; recording the feature sets into a database; matching the feature sets; and, testing and evaluating the system [
29]. The steps and their results are given in
Figure 3, respectively. Although the performance of the minutiae-based techniques relies on the accuracy of all these steps, the feature extraction and the use of sophisticated matching techniques to compare two minutiae sets are often more effective on the performance.
Global patterns of the ridges and valleys are compared to determine if the two fingerprints are aligned in the correlation-based AFIVSs. The template and query fingerprint images are spatially correlated to estimate the degree of similarity between them. The performance of correlation-based techniques is affected by non-linear distortions and noises in the image. In general, it has been observed that minutiae-based techniques perform better than correlation-based ones [
30]. The decision is made using the features that are directly extracted from the raw image in the image-based approaches that might be the only viable choice when image quality is too low to allow reliable minutiae extraction [
18].
Faces are probably the most highly accepted and user-friendly characteristics in the field of biometrics. Face recognition is an attractive and active research area with several applications ranging from static to dynamic [
19]. In general, a FRS consists of three main steps covering detection of the faces in a complicated background, extraction of the features from the face regions and localization of the faces and finally recognition tasks [
31]. The steps used in face processing in fingerprint to face task are illustrated in
Figure 4.
Face recognition process is really complex and difficult due to numerous factors affecting the appearance of an individual’s facial features such as 3D pose, facial expression, hair style, make-up,
etc. In addition to these varying factors, lighting, background, scale, noise and face occlusion, and many other possible factors make these tasks even more challenging [
31]. The most popular approaches to face recognition are based on each location and shape of the facial attributes including eyes, eyebrows, nose, lips and chin and their spatial relationships or the overall analysis of the face image representing a face as a weighted combination of a number of canonical faces [
4,
32]. Many effective and robust methods for the face recognition have been also proposed [
2,
19,
31–
35]. The methods are categorized in four groups as follows [
34]: human knowledge of what constitutes a typical face was encoded in the knowledge-based methods. Structural features that exist even when the pose, viewpoint or lighting conditions vary to locate faces were aimed to find in the feature invariant methods. Several standard patterns of a face were used to describe the face as a whole or the facial features separately in template matching based methods. Finally, appearance-based methods operate directly on images or appearances of the face objects and process the images as two-dimensional holistic patterns.
As explained earlier, processing fingerprints and faces are really difficult, complex and time consuming tasks. Many approaches, techniques and algorithms have been used for face recognition, fingerprint recognition and their sub steps. It is very clear from the explanations that dealing with generating faces from fingerprints are really more difficult tasks. Because of the tasks to be achieved in this article, faces, fingerprints, pre and post processing of them, applying many methods, implementing them in training and test procedures, analyzing them with different metrics, and representing the outputs in visual platform, etc. have made the prediction task more difficult.
5. Previous Work on Relationships among Fingerprints and Faces
In the light of explanations in the previous section, identical twins have strong similarities in both fingerprints and faces. Increasing and decreasing directions of these similarities are also the same among the people. Consequently, this similarity supports the idea that there might be some relationships among fingerprints and faces. The results reported by the authors have been also experimentally shown that relationships among fingerprints and faces exist [
5–
13].
In the studies [
5–
13], relationships among fingerprint and face parts were investigated and various face parts were tried to be predicted from just fingerprints step by step from simple to complex. At the beginning of the processes, authors have tried to generate only face borders [
5], only eyes [
13] and face contours [
6] from just fingerprints. In further steps of the process, the ANN structures were improved, trained and tested to predict static face parts [
7,
8,
12]. After these studies, ANN structures used in predicting process were advanced owing to the experiences of the authors and more complex face parts would be generated with high accuracy [
9–
11]. Finally, this study introduces for the first time the most complex representation of the relationships among fingerprints and faces. The studies [
5–
13] presented the experimental results in different platforms such as traditional evaluation platform, numerical evaluation platform and finally a visual evaluation platform. However it should be noted that because of having limited data sets covering 120 people in those studies, 10-fold cross-validation should be applied to illustrate the performance of the system. Randomly selected train-test data sets are no longer appropriate to characterize the performance of the system. It can lead into error in evaluating the performance of the system by causing imperfect comments on the results. In 10-fold cross validation process, the database was randomly divided into 10 different data group sets covering 90% of all data set in training and the rest 10% in test data sets for each fold. The proposed system was trained and tested with these ten different training-test data sets. After ten different trainings, 10 test processes were then followed. Accuracy and performance of the ANN models for each fold were computed according to the appropriate evaluation metrics covering expanded quantitative and qualitative metrics.
The ANN structures of previous studies were designed and reconfigured with randomly selected or experimentally obtained parameters. It is well known that finding appropriate parameters depending on applications is very difficult. It takes time and suitable parameters are established with the help of trails and errors. To do it systematically, as mentioned before, this study also presents obtaining best ANN parameters like numbers of the layers, numbers of the inputs, training algorithms and activation functions with the help of Taguchi experimental design technique.
In the previous studies [
5–
13], performance and accuracy of the proposed model are evaluated by quantitative metrics and/or human assessment presented in a graphical form. In this paper, both the quantitative measures (
i.e., objective) carried out automatically by computers expanding the metrics available in the literature and the qualitative (subjective) evaluation perceived by observation were taken into account. Next section describes these quantitative and qualitative evaluation metrics.
7. The Proposed System: Intelligent Biometric Feature Prediction System (IBFPS)
In order to achieve the task of prediction, a proposed system called IBFPS was developed and implemented. The new approach successfully generates total face features containing all of the face parts including eyebrows, eyes, nose, mouth and face contours including face border and ears from only fingerprints without having any information about faces in this study. In addition, the relationships among Fs&Fs are also analyzed and discussed in more details with the help of different evaluations criteria.
Assume that this relationship among faces and fingerprints can be mathematically represented as:
where
y is a vector indicating the feature set of the face model and its parameters achieved from a person,
x is a vector representing the feature set of the fingerprint acquired from the same person,
H(.) is a highly nonlinear system approximating
y onto
x. In this study,
H(.) is approximated to a model to generate the relationship among Fs&Fs with the help of ANN models.
The proposed system is based on MLP-ANN model having the best parameters with the help of Taguchi experimental design technique [
53–
55]. MLPs were trained with the binary input vectors and the corresponding output vectors with different parameter levels based on Mean Square Errors (MSEs) and Absolute Percentage Errors (APEs).
In order to determine the best parameters of MLP-ANN structure, L-16 (8
**1 2
**3) Taguchi experiment is designed. Taguchi design factors and factor levels were given in
Table 4. Training algorithms, the numbers of layers, the numbers of inputs and the transfer functions were main Taguchi design factors and 8, 2, 2 and 2 to be considered as factor levels, respectively.
MLP-ANN training algorithms considered and used in this work were Powell-Beale conjugate gradient back propagation (CGB), Fletcher-Powell conjugate gradient (CGF), Polak-Ribiere conjugate gradient (CGP), Gradient Descent (GD), Gradient Descent with adaptive learning coefficients (GDA), One Step Secant (OSS), GDA with momentum and adaptive learning coefficients (GDAM) and scaled conjugate gradient (SCG) [
56].
In this study, the numbers of layers were set to 3 and 4, the numbers of inputs were 200 and 300. Hyperbolic Tangent (HT) and Sigmoid Function (SF) activation functions were considered and used in MLP-ANN structures.
In Taguchi design, best parameters of MLP-ANNs were determined according to MSEs. Main effect plots were taken into considerations while analyzing the effects of parameters on the response factor. These plots might help to understand and to compare the changes in the level means and to indicate the influence of effective factors more precisely. According to these plots, training algorithms had the largest main effect on MSE. The numbers of layers in MLP-ANN structure, and transfer functions were also considerably effective. MSEs were not mainly affected by the numbers of inputs. Finally it can be clearly said that considering the main effect plots, MSEs will get smaller if the parameter settings given in
Table 5 were followed.
After the ANN structure and its training parameters were determined to achieve accurate solutions, the training processes were started with applying the fingerprint and face feature sets of the people to the system as inputs and outputs, respectively. The sizes of input and output vectors were also 300 and 176, respectively. The system achieves the training processes with these feature sets according to the learning algorithm and the ANN parameters which were obtained from Taguchi design method. Even if the feature sets of Fs&Fs were required in training, only fingerprint feature sets were used in test. It should be emphasized that these fingerprints used in test were totally unknown biometric data to the system. The outputs of the system for the unknown test data indicate the accuracy of the system. The success and reliability of the system must be clearly shown by evaluating the ANN outputs against the proper metrics in achieving face parts from fingerprints. The block diagram of the MLP-ANN used in this work is given in
Figure 9.
According to the best parameters obtained from Taguchi method, the MLP-ANN models were trained with a conjugate gradient algorithm that updates weight and bias values according to the conjugate gradient back propagation with Powell-Beale restarts (CGB). The CGB is a network training algorithm that updates weight and bias values according to the CGB algorithm [
56]. Conjugate gradient algorithms (CGAs) execute very effective search in the conjugate gradient direction. Generally, a learning rate is used to determine the length of the step size. For all CGAs, the search direction will be periodically reset to the negative of the gradient. The standard reset point occurs when the number of iterations is equal to the number of network parameters (weights and biases), but there are other reset methods that can improve the efficiency of training [
57]. One such reset method was proposed by Powell [
41], based on an earlier version proposed by Beale [
58].
In principle, feed forward neural networks for non-linear system identification can use all CGAs. In the first iteration, the CGAs start out by searching in the steepest descent direction that was given in
Equation (7):
In the equation,
po and
go are the search vector and gradient, respectively. Consider
xk is the estimate of the minimum at the start of the
k-th iteration. The
k-th iteration then consists of the computation of search vector
pk from which new estimate
xk+1 is obtained. It is given in
Equation (8):
In the equation, α
k is previous knowledge based upon the theory of the method or obtained by linear search. The next search direction is determined so that it is conjugate to previous search directions. Combining the new steepest descent direction with the previous search direction is the general way for determining the new search direction. It is given in
Equation (9). In the equation, β
k is a positive scalar and the various versions of gradient are distinguished by the manner constant β
k is computed [
59]:
Periodically resetting the search direction to the negative of the gradient improves the CGAs. Since Powell-Beale procedure is ineffective, a restarting method that does not abandon the second derivative information is needed. According to Powell-Beale technique it will restart if there is very little orthogonality left between the current gradient and the previous gradient. This is tested with the inequality given in
Equation (10). If this condition is satisfied, the search direction is reset to the negative of the gradient:
The inputs and outputs of the system were digital representations of fingerprints and faces of the people, respectively. The feature vectors of the fingerprints obtained from a commercially available software development kit contain the local and global feature sets of the fingerprints including singularities, minutiae points and their parameters [
60]. Detailed explanation of the feature extracting algorithms, extensive information of fingerprint feature sets and their storage format were given in the reference [
60]. These discriminative data represent the people with high accuracy. The outputs were the feature vectors of the faces obtained from a feature-based face feature extraction algorithm that was borrowed from Cox
et al. [61] and fundamentally modified and adapted to this application. Increasing the number of the reference points 35 to 88 helped to represent the faces more accurately and sensitively. Face feature sets were also shaped from Cartesian coordinates of the face model reference points not distances or average measures as given in the reference [61]. It was also observed that feature sets contain enough information about faces for getting them again with high accuracy. The face reference points on the template, on the face image of a person from our database and re-construction of the face model from the reference points were given in
Figure 10.
A flexible design environment for the face model re-construction converting the ANN outputs and/or the desired outputs to visual face models was also included in the software developed. Indeed, it basically transformed the reference points of the face models to the lines. The software is capable of plotting the results of actual and/or calculated values of the same face in the same platform or in different platforms. It also illustrates the ANN results on the real face images. So, the face model re-construction handles an important task for the system by creating two different visual evaluation platforms. This re-construction process enables users to achieve the qualitative evaluation processes easily, efficiently and automatically with the support of the developed useful graphical interface.
At the beginning of the experiment, an enrollment procedure was followed for collecting the biometric data from the people. This enrollment procedure helps to store fingerprint and face biometrics of individuals into the biometric system database. During this process a real multimodal database belonging to 120 persons was established. Ten fingerprints of each individual were scanned with a fingerprint scanner, and a 10 face image having different angles were also taken from the people using a digital camera. A set of examples including fingerprints and faces of an individual were given in
Figure 11 and
Figure 12, respectively. Only one frontal face image and one fingerprint belonging to the right hand index finger for each person were used in this study.
The software developed achieves all the tasks of the system from the enrollment step to evaluation step completely. It is expected that generating faces from fingerprints without having any priori knowledge about faces will find considerable attention in science and technology of biometrics, security and industrial applications.
As mentioned earlier, evaluating this system is very critical from the point of being a pioneering study claiming to generate the facial parts including the inner face parts with eyebrows and face contour with ears from only fingerprints. So, the success and reliability of the system must be clearly depicted. In that case, test processes in this article were mainly divided into two main parts: qualitative and quantitative evaluation platforms.
8. Experimental Results
In order to achieve the experiments effectively, automatically and easily, a software platform covering
Figures 3,
4 and
5 was developed.
In order to generate faces from only fingerprints, the following experiments were performed as:
Read fingerprints and faces from database
Obtain the feature sets of fingerprints and faces.
Establish a network configuration for training
Find optimum parameters with the help of Taguchi method.
Train the network with selected parameters.
Save the results for further uses.
Test the system against to the proper evaluation metrics.
Test the system performance based on 10-fold cross validation technique.
Investigate whether the quantitative (objective) evaluation results are consistent with qualitative (subjective) evaluations based on human perceptual assessment.
Previous experiments on predicting faces from fingerprints [
5–
13] have shown that the relationship between fingerprints and faces can be also achieved with high accuracy. In the current experiments, an automatic and intelligent system based on artificial neural network is designed to generate the faces of people from their fingerprints only. The proposed study has some advantages on the previous studies in the literature. These features are given below as:
All face parts including eyebrows, eyes, nose, mouth, face border and ears were successfully predicted in this study for the first time.
The optimal parameters of ANN model parameters were determined with the help of Taguchi experimental design technique.
Qualitative evaluation procedure was followed in addition to the quantitative evaluation procedure with some extra quantitative metrics.
10-fold cross validation technique was applied to analyze and to evaluate the performance and the accuracy of the system more precisely.
Producing the face models as close as possible to the real one is the most critical part of the system in this study. In order to evaluate the performance of the developed system effectively, test experiments were mainly focused on two qualitative and quantitative evaluation platforms: a 10-fold cross-validation method was followed, as mentioned earlier. The results of the system were tested against to these evaluation metrics.
FMR&FNMR and ROC curve representations were also given in
Figure 13. In the figure, ROC curves were plotted for each fold separately, but the FMR&FNMR representation curve was drawn using only average value of all folds for better comparison.
As can be seen in
Figure 13, the proposed system performs the tasks with high similarity measures to the desired values. According to the numerical results given in
Table 6, the proposed system was found also very successful.
The APE, MAE and MAPE values belonging to all test results for each fold of 10-fold cross validation were demonstrated in
Figure 14. Averages of all APEs, MAEs and MAPEs were given in
Figure 15.
For more realistic and comprehensive evaluation, all test results at each fold were illustrated in
Figure 16 with the desired values as used in the qualitative assessment method. Dark and light lines in the figure represent the desired and the generated face features, respectively. The number of rank orders in 10-fold cross validation with 20 participants as the results of the qualitative assessment method was given in
Table 7.
All observers who participated in our qualitative assessment method had normal (20/20) or corrected to normal acuity, normal color vision, and no history of ocular pathologies. In the qualitative assessment method each of the participants has assigned a numerical value of 1, 2, 3, 4 or 5 for all results of the each fold. Thus, within each condition, the system results were assigned 200 values (ten values per participant). In order to carry out a meaningful quantitative analysis, the rank frequency, that is, the number of times was assigned a rank value (
i.e., the number of all the ones, twos, threes, fours and fives for the results), was taken as the operational definition of perceived result quality for each fold. For each condition, the rank frequency was summed across the 10-folds, which resulted in the summed rank frequency (refer to line “Sum” in
Table 7). From
Table 7, it is clear that the proposed system was assigned the highest number of fives for all folds of 10-fold cross validation technique. According to the means of qualitative assessment method, the proposed system produced high quality results that were perceived to have the highest marks. Comparison for the folds of 10-fold cross validation technique can be also achieved using
Table 7. According to
Table 7, the first fold of the system was perceived to have the highest marks, tenth fold of the system produced imagery that was assigned the second highest number of fives (
i.e., essentially perceived as ‘second best’); and the seventh fold of the system produced imagery that was assigned the third highest number of fives (
i.e., essentially perceived as ‘third best’). For each condition the rank frequency was summed across the all folds of 10-fold cross validation technique.
Total value of the table indicates the sum of the marks for the all test results. It actually shows the overall system performance from point of the subjective manner. According to the total value, 47.5% of the participant gave 5, it means that they thought that “the results are nearly the same with the desired values, the system is”; 35.5% of the participant gave 4, it means they thought “the results are very similar to the desired values, the system is successful”, 14.5% of the participant gave 3, it also means that they thought “the results are similar to the desired values, the system success is average” and 2.5% of the participant gave 2, it means they thought “the results are a bit similar to the desired values, but the system cannot be accepted successful”. None of the participant gave 1, so no of them thought that the system is failed.
All obtained results from the two different evaluation platforms for each fold of 10-fold cross-validation technique have strongly demonstrated and clearly confirmed that there are close relationship among faces and fingerprints. Based on the results reported in this article in various forms, it can be clearly and confidently to declared that the proposed face model generation system is very successful in achieving face parts from only fingerprints. The system presented in this paper is a complete system combining all the other recent works introduced in [
5–
13], and it provides more complex and distinguished solution for generating the face parts. To the best of our knowledge, investigating relationships among fingerprints and face features including the all face parts has not been studied in the literature so far. Also it is the first study that was evaluated with 10-fold cross validation technique with qualitative evaluation metrics in addition to the quantitative evaluation metrics. Taguchi experimental design technique was also used to obtain best ANN parameters for better performance. Extensive experimental results have shown once more that the proposed system yields superior performance and it is capable of efficiently generating the face masks from only fingerprints.
9. Conclusions and Future Work
Predicting complete face features with high accuracy just from fingerprints is the principal objective of this paper. In this study a novel approach was developed, used and introduced to successfully achieve this aim. In the proposed study, the relationships among fingerprint and face biometrics were established and an unknown biometric feature was also predicted with high accuracy from a known biometric feature. The results of the two main validation tests proved that the proposed system is very successful in automatically generating the faces from only fingerprints. This study is an improved version of our earlier studies.
In the future research, investigations will be conducted to enhance the face generation process. In addition, a larger multi-modal database with international participants including Fs&Fs will be collected to investigate the proposed approach. Even if an unknown biometric feature can be achieved from a known biometric feature, the achieved feature cannot represent faces in real time face pictures. This initial study might help to lead to create new concepts, research areas, and especially new applications in the field of biometrics.
Comparing with the results given in the literature determining the best parameter settings by Taguchi experimental design technique has improved the results significantly. In addition, it should be noted that predicting more face parts from fingerprints reduced the prediction performance of the system.
For a more objective comparison, the performance and accuracy of the system have been evaluated with 10-fold cross validation technique using qualitative evaluation metrics in addition to the expanded quantitative evaluation metrics. Consequently, the results were presented on the basis of the combination of these objective and subjective metrics for illustrating the qualitative properties of the proposed methods as well as a quantitative evaluation of their performances.