Intelligent Biometric Techniques In Fingerprint And Face Recognition Pdf
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- Handbook of Fingerprint Recognition
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- Intelligent biometric techniques in fingerprint and face recognition
Handbook of Fingerprint Recognition
This study presents a new approach based on artificial neural networks for generating one biometric feature faces from another only fingerprints. An automatic and intelligent system was designed and developed to analyze the relationships among fingerprints and faces and also to model and to improve the existence of the relationships.
The new proposed system is the first study that generates all parts of the face including eyebrows, eyes, nose, mouth, ears and face border from only fingerprints. It is also unique and different from similar studies recently presented in the literature with some superior features.
The parameter settings of the system were achieved with the help of Taguchi experimental design technique. The performance and accuracy of the system have been evaluated with 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. Experimental results have shown that one biometric feature can be determined from another. These results have once more indicated that there is a strong relationship between fingerprints and faces. 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. 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 ]. 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, 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. Finally, the proposed work is concluded and discussed in Section 8.
Biometric features covering physical or behavioral characteristics including fingerprint, face, ear, hand geometry, voice, retina, iris recognition, etc. 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.
Data acquisition, verification, identification and screening phases are the main types of biometric based systems [ 4 ]. The types are summarized as:. Type I: The biometric data acquisition phase is the first step of the other three phases. Enrollment, classification and recording of the biometric features are achieved in this phase. Type II: The verification phase is the most commonly used biometric system mode in the social life like person identification systems in physical access control, computer network logon or electronic data security [ 2 , 4 ].
At the end of the verification phase, the submitted claim of the identity is either rejected or accepted [ 1 ]. Type III: The identification phase is commonly used in applications requiring high security tasks like criminal identification and police work. The system fails if the person is an undefined person in the system database.
In that case, the output of the system is a combination list of identities and the scores indicates the similarity among two biometric features [ 15 ]. According to some pre-defined rules about similarity measures, the system decision was produced in this phase. Type IV: The screening phase is like the identification phase.
The results of determination whether a person belongs to a watch list of identities or not is displayed in this phase. Security at airports, public events and other surveillance applications are some of the screening examples [ 4 , 16 ].
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 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.
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. 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. ANNs are biologically inspired intelligent techniques to solve many problems [ 36 — 40 ]. Learning, generalization, less data requirement, fast computation, ease of implementation and software and hardware availability features have made ANNs very attractive for many applications [ 36 ].
There has been a growing research interest in security and recognition applications based on intelligent techniques and especially ANNs which are also very popular in biometric-based applications [ 5 — 13 , 29 , 34 , 35 , 37 — 40 ]. Multilayered perceptron MLP is one of the most popular ANN architectures and can be trained with various learning algorithms. Because an MLP structure can be trained by many learning algorithms, it has been successfully applied to a variety of problems in the literature [ 36 ].
The MLP structure consists of three layers: input, output and hidden layers. One or more hidden layers might be used. The neurons in the input layer can be treated as buffers and distribute input signal to the neurons in the hidden layer.
A facial recognition system is a technology capable of matching a human face from a digital image or a video frame against a database of faces, typically employed to authenticate users through ID verification services , works by pinpointing and measuring facial features from a given image. While initially a form of computer application , facial recognition systems have seen wider uses in recent times on smartphones and in other forms of technology, such as robotics. Because computerized facial recognition involves the measurement of a human's physiological characteristics facial recognition systems are categorised as biometrics. Although the accuracy of facial recognition systems as a biometric technology is lower than iris recognition and fingerprint recognition , it is widely adopted due to its contactless process. Automated facial recognition was pioneered in the s. Their early facial recognition project was dubbed "man-machine" because the coordinates of the facial features in a photograph had to be established by a human before they could be used by the computer for recognition.
The four coauthors have a distinguished combination of academic and professional experience Overall, readers will be pleased with the style and substance of this book. The thoroughness of the treatment of biometric methods is not obvious from the title. This feature will make the book particularly valuable in some robotics contexts. The book is suggested as a reference book for a graduate course on biometrics. The material is clearly presented …. This will certainly be a standard reference work in its field.
Intelligent biometric techniques in fingerprint and face recognition
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This study presents a new approach based on artificial neural networks for generating one biometric feature faces from another only fingerprints. An automatic and intelligent system was designed and developed to analyze the relationships among fingerprints and faces and also to model and to improve the existence of the relationships. The new proposed system is the first study that generates all parts of the face including eyebrows, eyes, nose, mouth, ears and face border from only fingerprints. It is also unique and different from similar studies recently presented in the literature with some superior features. The parameter settings of the system were achieved with the help of Taguchi experimental design technique.
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