Analysis of the ten key technologies of biometrics
Biometrics technology has made great progress in recent years, but in order to make biometrics from theoretical research to practical applications, many research institutes need to break through and solve a series of key technologies. From a statistical point of view, human fingerprints, palms, irises and other physiological features are unique. Therefore, these features can be used as the basis for identifying the identity of the user.
1. Biometric sensor technology
Biometrics can be measured by a certain principle and converted into digital signals that can be processed by computers. This is the main task of biometric sensors and the first step in biometrics. Most of the biometric features are image signals formed by optical sensors such as CCD or CMOS, such as faces, fingerprints, irises, palm prints, hand shapes, veins, and the like.
However, iris and vein images require an active infrared source to obtain clear personality characteristics. Since the active light source can overcome the influence of visible light changes on biological characteristics, researchers have recently designed infrared imaging devices in the field of face recognition to overcome the intra-class differences in face patterns with illumination changes, thereby greatly improving people. The accuracy of face recognition.
In order to improve the ease of use, comfort and user acceptance of biometric systems, while ensuring the quality of biometric signals, in addition to being small and sophisticated, and cost-effective, biometric sensor technology has many areas for improvement. For example, 3D fingerprint sensor technology has been recently acquired through contactless methods. The core technologies of biometric sensors include:
The biometric acquisition device must allow the user and the recognition system to be at appropriate distances and locations to capture qualified biometric signals. The most ideal solution is to let the acquisition device automatically determine the position of the user, and then actively adjust the optical system or directly move the collection device through the mechanical device, so that the requirements of the user can be reduced, and the collection method is more intelligent and user-friendly.
2, living body detection technology
In order to prevent a malicious person from forging and stealing other people's biometrics for identity authentication, the biometric identification system must have a living body detection function, that is, whether the biometrics submitted to the system are from a living individual. The biometric biometric discrimination technology utilizes people's physiological characteristics. For example, biometric fingerprint detection can be based on finger temperature, perspiration, and electrical conductivity. Live face detection can be based on head movement, respiration, red eye effect, etc. The in vivo iris detection can be based on the characteristics of the iris vibration, the motion information of the eyelashes and the eyelids, and the contraction and expansion response characteristics of the pupil to the intensity of the visible light source.
In addition, spectroscopy information based on biometric images is also an effective way to perform in vivo detection. For example, a printed image will form a regular paper texture feature that can be detected using spectral features. In addition, the living characteristics of biometrics can be detected in the form of human-computer interaction; the use of multimodal biometrics can also increase the difficulty of forgery.
From the current technical level, the living body detection function has always been a weak link in the biometric identification system. Researchers have used fake fingerprints and human faces to break the existing system, which has caused some users to have a crisis of trust in biometrics. Therefore, the living detection technology will be the biggest bottleneck for biometric systems to enter high-end security applications.
3. Biometric signal quality evaluation technology
In an automatic identification system, biometrics are typically acquired in the form of a continuous video stream or audio stream. Since the effective biometric collection range is always limited, coupled with human motion, posture changes and other factors, most of the biometric signals transmitted to the computer are unqualified. High-quality biometric signals are the basis for feature representation and identification. Low-quality biometric signals may cause false reception or false rejection, reducing system stability and robustness (system robustness), and wasting a lot of The computational resources are on invalid biometric signal processing.
The quality evaluation of biometric signals can be seen as a two-class pattern recognition problem—dividing the collected biometrics into qualified and unqualified cases. If the quality of the qualified signal is to be scored, the evaluation index should also be quantified.
The problem of quality evaluation of biometric signals is a difficult problem, because the causes of poor quality of characteristic signals vary widely, that is, there are too many types of negative samples, and it is difficult to design a classifier to distinguish all positive and negative samples. . Low-quality biometrics that need to be filtered by quality assessment generally include images with defocus or motion blur, signals with too low signal-to-noise ratio, images that are occluded, and the like. The quality evaluation algorithm can generally be designed from both the airspace and the frequency domain.
4. Biosignal localization and segmentation technology
The original signal collected from the biometric acquisition device generally includes not only the biological features themselves, but also background information, such as the original iris image including iris, pupil, sclera, eyelid and eyelashes, which can effectively identify people. The image content is also in the iris area. Therefore, the content of interest must be segmented from the original signal for feature extraction. Positioning and segmentation algorithms are generally based on a priori knowledge of biometrics in terms of image structure and signal distribution. For example, face detection is to find and locate the face area from the image, which has always been a research hotspot in the field of computer vision.
5. Biometric signal enhancement technology
After the segmented feature regions are obtained, some biometric methods need to enhance the region of interest before feature extraction. The main purposes include denoising and highlighting feature content. For example, face and iris images generally use the histogram equalization method to enhance the contrast of image information; fingerprints generally use the frequency domain method to obtain the frequency and direction features of the ridge line distribution and then enhance the texture; for relatively fuzzy biometric signals, Consider using super-resolution methods or inverse filtering methods for enhancement.
6. Calibration technology for biometric signals
In order to overcome the translation, scale and rotational transformation between biometric signals acquired at different times, it is necessary to align the two biometrics participating in the alignment. Some biometric calibrations are performed before feature extraction, such as the ActiveShape Model and the Active Appearance Model for face alignment; some biometric calibration processes are the process of feature matching. The calibration result of the biometric signal has a great influence on the recognition accuracy, so some scholars believe that the most important problem of biometric recognition is the calibration technique.
7. Biometric expression and extraction techniques
For biometrics, whether it is a layman or a layman, the first question people think about is: What features are used to identify the machine? What is the essential feature of the biometric signal that highlights the individualized difference? This is the basic of biometrics. The problem of principle.
For this problem, there is a consensus in the field of individual biometrics, such as fingerprint recognition. It is recognized that the detail points (including the tip point and the bifurcation point) are the best way to describe the fingerprint features, so there is a unified basis based on the international The fingerprint feature template exchange standard of the detailed point information brings convenience to the compatibility and data exchange of fingerprint identification systems of different manufacturers.
However, in other fields of biometrics, such as face, iris, palm print and other fields, researchers are constantly exploring the best feature expression models. Although there are many kinds of feature expression methods in these fields, some algorithms have achieved good recognition performance, but the fundamental problem of face recognition, iris recognition, palmprint recognition - "What is a face, iris or palm print image? The essential characteristics and their effective expression?" has never been answered with authority and universal approval.
This is because the feature expression methods of each face, iris and palmprint image are based on a certain signal processing method or a certain computer vision or a pattern recognition theory, "public theory is reasonable, woman is reasonable", everyone There has been no in-depth study of the essential features of these images.
Nowadays, the trend in the field of biometric expression is to try all kinds of classical or newly proposed image analysis methods in order, and it is a bit of a feeling of big luck. The root of this phenomenon is that everyone has no guidance on basic theory. Work hard in the direction. Since the various methods are “politicalâ€, it is difficult to unify and standardize the data exchange format of the biometric template. For example, data exchange standards for faces, irises, and palm prints can only be based on images, because you can't find a unified, authoritative representation of image features.
8, biometric matching technology
Feature matching is the calculation of the similarity between the feature vectors of two biometric samples. The graph matching algorithm is also successfully applied in the similarity measure of fingerprint minutiae mode, face mode, and iris plaque mode.
9. Biometric database retrieval and classification technology
With the popularity of biometrics in human daily life, the growth in the number of users will inevitably lead to the expansion of the biometric database. This scale of expansion is not only manifested in the expansion of data storage, but also in the increase in the time it takes to search for a record from the database. For example, in a biometric application that is one-to-many (such as a city, a country, or an industry), the length of time to complete a recognition will be unbearable. This is an inevitable problem when any mature biometric technology transforms from small-scale applications to large-scale applications.
10. Performance evaluation of biometric identification system
To date, any biometric system or method has the potential to go wrong. It is a very complicated problem to give objective and accurate evaluation of the recognition accuracy of the system. It is affected by factors such as the quantity, quality and evaluation index of the test samples, but this is a concern for the application unit and the judicial department. Focus.
Therefore, the performance evaluation of biometric methods has become an important direction of biometrics research. For the 1:1 alignment authentication system, there are two cases of error: one is to identify different people's biometrics as the same class, called error reception; the other may be to identify the same person's biometrics as different classes. , called error rejection.
In order to make biometric technology applicable in places with high security requirements, in addition to algorithm design, it is also important to protect the security of the system itself and improve the resistance to various hacker attacks. In order to improve the security of the recognition system, encryption, digital signature, time stamping and other methods for biometric data, feature templates and applications will be a feasible research direction.
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