- Количество слайдов: 59
Fusion by Biometrics 主講人：李佳明、陳明暘 指導教授：林維暘
Outline n n n n n Introduction Biometric system Feature extraction The advantage of verification in biometrics The flow of verification Fusion methods Experiment Results Conclusion Reference
Introduction n n Multimodal biometrics systems consolidate the evidence presented by multiple biometric sources and typically provide better recognition performance compared to systems based on a single biometric modality. Multi-biometrics systems provide anti-spoofing measures by making it difficult for an intruder to spoof multiple biometric traits simultaneously.
Comparison of various biometric technologies (H=High, M=Medium, L=Low) Biometrics Universality Uniqueness Permanence Collectability Performance Acceptability Circumvention Face H L M H L Fingerprint M H Hand geometry M M M H M M M Keystrokes L L L M M Hand veins M M M H Iris H H H M H L H Retinal scan H H M L H Signature L L L H L Voice M L L M L H L facial thermogram H H L H M H H Odor H H H L L M L DNA H H H L L Gait M L L H M Ear recognition M M H M
The advantage of Multimodal Biometric n Multiple biometric sources enhance matching performance. n Reducing failure to enroll rate. n Difficult to spoof multiple traits simultaneously.
A biometric system
A biometric system n A biometric-based authentication system operates in two modes n 1. Enrollment mode n 2. Authentication mode
A biometric system n 1. Enrollment： n n A user’s biometric data is acquired using a biometric reader and stored in a database. The stored template is labeled with a user identity (e. g. , name, identification number, etc. ) to facilitate authentication.
A biometric system n 2. Authentication： n A user’s biometric data is once again acquired and the system uses this to either identify who the user is, or verify the claimed identity of the user. n n Identification：Comparing the acquired biometric information against templates corresponding to all users in the database. Verification：Comparison with only those templates corresponding to the claimed identity.
A biometric system
Feature extraction n n Fingerprint Face Hand Geometry Iris
Feature extraction n Fingerprint friction ridge details are generally described in a hierarchical order at three different levels： n n Level 1 - pattern Level 2 - minutia points Level 3 - pores and ridge contours Automated Fingerprint Identification Systems (AFIS) currently rely on Level 1 and Level 2 features.
Feature extraction n Level 1 features, or patterns, are the macro details of the fingerprint such as ridge flow and pattern type.
Feature extraction n Level 2 features, or points, refer to the Galton characteristics or minutiae, such as ridge bifurcations and endings.
Feature extraction n Level 3 features, or shape, include all dimensional attributes of the ridge such as ridge path deviation, width, shape, pores, edge contour, incipient ridges, breaks, creases, scars, and other permanent details.
Feature extraction n Fingerprint image resolution. The same fingerprint captured at three different image resolutions n n n (a) 380 ppi (Identix 200 DFR) (b) 500 ppi (Cross. Match ID 1000) (c) 1, 000 ppi (Cross. Match ID 1000).
Feature extraction n n Different levels of fingerprint features detected. Level 3 features are matched using the ICP algorithm.
Feature extraction n Reference point (X), the region of interest, and 80 sectors (B = 5, k = 16) superimposed on a fingerprint
Feature extraction n n Face recognition is an important biometric identification technology. Facial scan is an effective biometric attribute/indicator. The performance of face recognition systems dependent on consistent conditions such as lighting, pose and facial expression.
Feature extraction n Preprocessing n n 幾何位置的調整 - 取人臉影像中兩個control point， 分別為左眼的中心點和右眼的中心點，利用這兩個控 制點。 明亮度的調整 - histogram equalization，此步驟是為 了縮小各張影像之間亮度的改變所造成的差異
Feature extraction n n 擷取三個人臉區域，在每個區域裡面，全部的 影像灰階值都會被儲存在一個向量，該向量就 是該區域的特徵向量。 利用了 Principal Component Analysis (PCA)將 特徵向量降維。
Feature extraction n Automatic feature extraction for 3 D face matching.
Feature extraction n Segmentation of facial scan.
Feature extraction n For a frontal facial scan, nose tip usually has the largest z value.
Feature extraction n Pose angle quantization. n Example of directional maximum.
Feature extraction n extracted nose profiles.
Feature extraction n Feature extraction results which lead to correct 3 D face matches.
Feature extraction n Some biometrics may not be acceptable for the sake of protecting an individual's privacy. As hand geometry information is not very distinctive, it is distinctive enough for verification but not for identification. It is simple method of sensing which does not impose undue requirements on the imaging optics.
Feature extraction n n Hand geometry sensing device. 5 images of the same hand are taken.
Feature extraction n n Hand shape alignment We represent the shape of a hand by a set of ordered points in the Euclidean plane.
Feature extraction n The fourteen axes along which feature values are computed.
Feature extraction n The iris is a protected internal organ whose random texture is complex, unique, and stable throughout life. It can serve as a kind of living passport or password that one need not remember but can always present. "Biometric Personal Identification System Based on Iris Analysis. " U. S. Patent No. 5, 291, 560 issued March 1, 1994 (J. Daugman).
Feature extraction n Finding an Iris in an Image n n n minimum of 70 pixels in iris radius. Iris radius of 80 to 130 pixels has been more typical. Monochrome CCD cameras (480 x 640) have been used. using a coarse-to-fine strategy terminating in singlepixel precision estimates of the center coordinates and radius of both the iris and the pupil.
Feature extraction n The outline overlay shows results of the iris and pupil localization.
Feature extraction n Iris Feature Encoding by 2 D Wavelet Demodulation. n n Each isolated iris pattern is then demodulated to extract its phase information using quadrature 2 D Gabor wavelets. This process is repeated all across the iris with many wavelet sizes, frequencies, and orientations, to extract 2, 048 bits.
Feature extraction n Advantages of the Iris for Identification n n n Highly protected, internal organ of the eye. Externally visible; high degree of randomness. Pre-natal morphogenesis (7 th month of gestation) Limited genetic penetrance of iris patterns Patterns apparently stable throughout life Encoding and decision-making are tractable
Feature extraction n Disadvantages of the Iris for Identification n n n Small target to acquire from a distance Located behind a curved, wet, reflecting surface Obscured by eyelashes, lenses, reflections Partially occluded by eyelids, often drooping Deforms non-elastically as pupil changes size Illumination should not be visible or bright
A biometric system has four important components 1. Sensor module : n Acquire the biometric data of an individual. 2. Feature extraction module : n Acquire data is processed to extract feature values.
A biometric system has four important components 3. Matching module : n Feature values are compared against those in the template by generating a matching score. 4. Decision-making module : n The user’s identity is established or a claimed identity is either accepted or rejected based on the matching score generated in the matching module.
Fusion in biometrics (1) Fusion at the feature extraction level : n n n 1. The data obtained from each sensor is used to compute a feature vector. 2. Concatenate the two vectors into a single new vector. 3. Feature reduction techniques may be employed.
Multimodal biometric system A prototype multimodal biometric system.
Fusion in biometrics (2) Fusion at the matching score level : n n Each system provides a matching score indicating the proximity of the feature vector with the template vector. These scores can be combined to assert the veracity of the claimed identity.
Fusion in biometrics (3) Fusion at the decision level: n n Each sensor can capture multiple biometric data and the resulting feature vectors individually classified into the two classes –– accept or reject. A majority vote scheme can be used to make the final decision.
Fusion in biometrics n Fusion in the context of biometrics can take the following forms : n (1) Single biometric multiple representation. n (2) Single biometric multiple matchers. n (3) Multiple biometric fusion.
Fusion in biometrics n (1) Single biometric multiple representation. n n This type of fusion involves using multiple representations on a single biometric indicator. Typically, each representation has its own classifier.
Fusion in biometrics n (2) Single biometric multiple matchers. n It is also possible to incorporate multiple matching strategies in the matching module of a biometric system and combine the scores generated by these strategies.
Fusion in biometrics n (3) Multiple biometric fusion. n n By integrating matching scores obtained from multiple biometric sources. These include majority voting, sum and product rules, k-NN classifiers, SVMs, decision trees, Bayesian methods, etc.
Fusion in biometrics n (4) Others n n 1. Store multiple templates in database. Example : A fingerprint biometric system may store multiple templates of a users fingerprint (same finger) in its database. When a fingerprint impression is presented to the system for verification, it is compared against each of the templates, and the matching score generated by these multiple matchings are integrated.
Fusion in biometrics n (4) Others n n 2. A system may store a single template of a users finger, but acquire multiple impressions of the finger during verification. 3. Another possibility would be to acquire and use impressions of multiple fingers for every user.
Experiment Results n n n 50 users Biometrics : fingerprint , face , hand geometry Sum rule
Conclusion n n Multimodal biometric systems provide better recognition performance. Different users tend to adopt differently to individual biometric indicators. These weights can be learnt over time by examining the stored template of the user.
Reference 1. http: //en. wikipedia. org/wiki/Biometric 2. Score Normalization in Multimodal Biometric Systems (by Anil Jain , Karthik Nandakumar , Arun Ross) 3. Information fusion in biometrics (by Arun Ross , Anil Jain) 4. http: //biometrics. cse. msu. edu/
Reference 5. http: //www. cl. cam. ac. uk/~jgd 1000/ 6. A. K. Jain and N. Duta, "Deformable matching of hand shapes for verification", Proceedings of IEEE International Conference on Image Prcoessing, October 25 -28, Kobe, Japan, 1999. 7. A. K. Jain and N. Duta, "Deformable matching of hand shapes for verification", Proceedings of IEEE International Conference on Image Prcoessing, October 25 -28, Kobe, Japan, 1999. 8. A. K. Jain, Y. Chen and M. Demirkus, "Pores and Ridges: High Resolution Fingerprint Matching Using Level 3 Features", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 1, pp. 15 -27, January 2007.