Fingerprint classification reduces the search space of a large fingerprint database by partitioning it into smaller subsets based on class. Several approaches for fingerprint classification have been proposed, including those based on singular points, ridge structure, frequency analysis, and mathematical models. A new approach uses orientation field flow curves derived from the orientation field and classifies fingerprints based on analyzing the tangent space isometric maps of the curves. This approach achieved 94.4% accuracy on the NIST 4 database, comparable to state-of-the-art methods. Future work includes extending the classification to more classes and investigating other indexing techniques.
FLIR Systems FLIR ONE & LEPTON Consumer Thermal Imager with Microbolometer te...Yole Developpement
Initially focused on the Military market, uncooled thermal camera sales have grown significantly due to the substantial cost reduction of microbolometers and growing adoption in commercial markets. With a vertically-integrated business model and a fabless structure, FLIR drive the commercial market’s price war.
Plugged into the back of an IPhone 5 or 5S, the FLIR ONE is the first consumer thermal camera featuring Long Wave Infrared (LWIR) technology. It contains a visible VGA (640x480) camera and a thermal camera which provide images blended using FLIR MSX Technology.
The thermal camera uses a new core, LEPTON, featuring a 80x60 pixels resolution with pixel size of 17µm. The sensor technology in the LEPTON core is an uncooled VOx microbolometer. Thanks to its strong integration at the core level with innovative wafer-level optics (WLO), wafer-level packaging (WLP) and custom ASIC use, this is the world's smallest microbolometer-based thermal imaging camera core.
Based on complete teardown analyses of the FLIR ONE and the LEPTON core, the reports provide the bill-of-material (BOM) and the manufacturing cost of the infrared camera as well as a complete physical analysis and manufacturing cost estimation of the infrared module.
The report also includes a comparison with FLIR i7 infrared camera and sensor, highlighting the technical choices made by FLIR to decrease by more than three the manufacturing cost at the camera level and the sensor level to made a consumer product.
FLIR Systems FLIR ONE & LEPTON Consumer Thermal Imager with Microbolometer te...Yole Developpement
Initially focused on the Military market, uncooled thermal camera sales have grown significantly due to the substantial cost reduction of microbolometers and growing adoption in commercial markets. With a vertically-integrated business model and a fabless structure, FLIR drive the commercial market’s price war.
Plugged into the back of an IPhone 5 or 5S, the FLIR ONE is the first consumer thermal camera featuring Long Wave Infrared (LWIR) technology. It contains a visible VGA (640x480) camera and a thermal camera which provide images blended using FLIR MSX Technology.
The thermal camera uses a new core, LEPTON, featuring a 80x60 pixels resolution with pixel size of 17µm. The sensor technology in the LEPTON core is an uncooled VOx microbolometer. Thanks to its strong integration at the core level with innovative wafer-level optics (WLO), wafer-level packaging (WLP) and custom ASIC use, this is the world's smallest microbolometer-based thermal imaging camera core.
Based on complete teardown analyses of the FLIR ONE and the LEPTON core, the reports provide the bill-of-material (BOM) and the manufacturing cost of the infrared camera as well as a complete physical analysis and manufacturing cost estimation of the infrared module.
The report also includes a comparison with FLIR i7 infrared camera and sensor, highlighting the technical choices made by FLIR to decrease by more than three the manufacturing cost at the camera level and the sensor level to made a consumer product.
FINGERPRINT CLASSIFICATION BASED ON ORIENTATION FIELDijesajournal
ABSTRACT
This paper introduces an effective method of fingerprint classification based on discriminative feature gathering from orientation field. A nonlinear support vector machines (SVMs) is adopted for the classification. The orientation field is estimated through a pixel-Wise gradient descent method and the percentage of directional block classes is estimated. These percentages are classified into four-dimensional vector considered as a good feature that can be combined with an accurate singular point to classify the fingerprint into one of five classes. This method shows high classification accuracy relative to other spatial domain classifiers.
FACE RECOGNITION ALGORITHM BASED ON ORIENTATION HISTOGRAM OF HOUGH PEAKSijaia
In this paper we propose a novel face recognition algorithm based on orientation histogram of Hough Transform Peaks. The novelty of the approach lies in utilizing Hough Transform peaks for determining the orientation angles and computing the histogram from it. For extraction of feature vectors first the images are divided into non overlapping blocks of equal size. Then for each of the blocks the orientation histograms are computed. The obtained histograms are combined to form the final feature vector set. Classification is done using k nearest neighbor classifier. The algorithm has been tested on the ORL
database, Yale B Database & the Essex Grimace Database.97% Recognition rates have been obtained for
ORL database, 100% for Yale B and 100% for Essex Grimace database
False Peaks Occuring at Direction-Finding Via Cylindrical Antenna Array with ...IJRESJOURNAL
ABSTRACT: In this paper the problem of DOA estimation methods for cylindrical antenna arrays is considered. The performances are estimated in various noise environments and for various geometries of the antenna arrays. Additionally the problem of false peaks occurring in spatial spectrum is closely considered. Probability of occurring false peaks after computer simulations is presented.
Computational Tools for Extracting, Representing and Analyzing Facial Featuressaulnml
ABSTRACT: In this work, we present a computer aided system for interactive extraction of anthropometrical points (landmarks) on 3D human face meshes, and a methodology for a statistical analysis of the anthropometrical points. In the developed software, we employed real time rendering techniques and interactive picking through collisions detection and haptic feedback, to allow intuitive user interaction with the virtual model. We also exploit the geometric information of the meshes by computing and displaying the curvatures and shape index, to give to the user a better understanding of the 3D data by using color maps. The proposed method was tested on a database of 35 faces from healthy Mexican individuals, obtained with a low cost structured light stereovision system. One of the objectives of this study, was to determine the statistical variability of a set of 19 face landmarks, which define facial features of the eyes, mouth, nose, cheeks and chin. To validate the reliability of the hand-extracted landmarks, a Technical Error of Measurement (TEM) analysis was performed. After the points were extracted, a rigid registration of the landmarks, to those from a reference head model, was applied by determining an optimal rigid transformation consisting of a unit quaternion and a translation vector obtained from the cross-covariance matrix. To obtain the mean landmarks set and the modes of variation, a Principal Components Analysis (PCA) based on the covariance matrix was employed. Finally, we approximated the average facial shape of the population under study, by deforming the reference head model through cage-based registration. In such approach, the high resolution model is attached to a rough mesh or cage, which encloses the detailed model, by using Mean Value Coordinates (MVC) each vertex of the detailed mesh is represented as a linear combination of the cage mesh vertices, which allow detail preserving deformation of the high polygonal mesh by displacement of the vertices from the coarse mesh, then the cage is iteratively deformed through Laplacian deformation in order to minimize the squared distance between corresponding landmarks. Besides working with points, and linear and angular measurements, the developed software also allows interactively to select paths and contours on the mesh surface, obtaining geodesic distances and areas; and even working on images. Finally, our work can be extended for the analysis of other complex anatomical structures, and potentially it may have other applications such as facial recognition on forensics, random face generation for avateering in virtual environments, and building compact 3D face databases.
SVM Based Identification of Psychological Personality Using Handwritten Text IJERA Editor
Identification of Personality is a complex process. To ease this process, a model is developed using cursive
handwriting. Area based, width based and height based thresholds are set for only character selection, word
selection and line selection. The rest is considered as noise. Followed by feature vector construction. Slope
feature using slope calculation, shape features and edge detection done using Sobel filter and direction
histogram is considered. Based on the direction of handwriting the analysis was done. Writing which rises to
the right shows optimism and cheerfulness. Sagging to the right shows physical or mental weariness. The lines
which are straight, reveals over-control to compensate for an inner fear of loss of control.The analysis was done
using single line and multiple lines. Simple techniques have provided good results. The results using single line
were 95% and multiple lines were 91%.The classification is done using SVM classifier.
FINGERPRINT CLASSIFICATION BASED ON ORIENTATION FIELDijesajournal
ABSTRACT
This paper introduces an effective method of fingerprint classification based on discriminative feature gathering from orientation field. A nonlinear support vector machines (SVMs) is adopted for the classification. The orientation field is estimated through a pixel-Wise gradient descent method and the percentage of directional block classes is estimated. These percentages are classified into four-dimensional vector considered as a good feature that can be combined with an accurate singular point to classify the fingerprint into one of five classes. This method shows high classification accuracy relative to other spatial domain classifiers.
FACE RECOGNITION ALGORITHM BASED ON ORIENTATION HISTOGRAM OF HOUGH PEAKSijaia
In this paper we propose a novel face recognition algorithm based on orientation histogram of Hough Transform Peaks. The novelty of the approach lies in utilizing Hough Transform peaks for determining the orientation angles and computing the histogram from it. For extraction of feature vectors first the images are divided into non overlapping blocks of equal size. Then for each of the blocks the orientation histograms are computed. The obtained histograms are combined to form the final feature vector set. Classification is done using k nearest neighbor classifier. The algorithm has been tested on the ORL
database, Yale B Database & the Essex Grimace Database.97% Recognition rates have been obtained for
ORL database, 100% for Yale B and 100% for Essex Grimace database
False Peaks Occuring at Direction-Finding Via Cylindrical Antenna Array with ...IJRESJOURNAL
ABSTRACT: In this paper the problem of DOA estimation methods for cylindrical antenna arrays is considered. The performances are estimated in various noise environments and for various geometries of the antenna arrays. Additionally the problem of false peaks occurring in spatial spectrum is closely considered. Probability of occurring false peaks after computer simulations is presented.
Computational Tools for Extracting, Representing and Analyzing Facial Featuressaulnml
ABSTRACT: In this work, we present a computer aided system for interactive extraction of anthropometrical points (landmarks) on 3D human face meshes, and a methodology for a statistical analysis of the anthropometrical points. In the developed software, we employed real time rendering techniques and interactive picking through collisions detection and haptic feedback, to allow intuitive user interaction with the virtual model. We also exploit the geometric information of the meshes by computing and displaying the curvatures and shape index, to give to the user a better understanding of the 3D data by using color maps. The proposed method was tested on a database of 35 faces from healthy Mexican individuals, obtained with a low cost structured light stereovision system. One of the objectives of this study, was to determine the statistical variability of a set of 19 face landmarks, which define facial features of the eyes, mouth, nose, cheeks and chin. To validate the reliability of the hand-extracted landmarks, a Technical Error of Measurement (TEM) analysis was performed. After the points were extracted, a rigid registration of the landmarks, to those from a reference head model, was applied by determining an optimal rigid transformation consisting of a unit quaternion and a translation vector obtained from the cross-covariance matrix. To obtain the mean landmarks set and the modes of variation, a Principal Components Analysis (PCA) based on the covariance matrix was employed. Finally, we approximated the average facial shape of the population under study, by deforming the reference head model through cage-based registration. In such approach, the high resolution model is attached to a rough mesh or cage, which encloses the detailed model, by using Mean Value Coordinates (MVC) each vertex of the detailed mesh is represented as a linear combination of the cage mesh vertices, which allow detail preserving deformation of the high polygonal mesh by displacement of the vertices from the coarse mesh, then the cage is iteratively deformed through Laplacian deformation in order to minimize the squared distance between corresponding landmarks. Besides working with points, and linear and angular measurements, the developed software also allows interactively to select paths and contours on the mesh surface, obtaining geodesic distances and areas; and even working on images. Finally, our work can be extended for the analysis of other complex anatomical structures, and potentially it may have other applications such as facial recognition on forensics, random face generation for avateering in virtual environments, and building compact 3D face databases.
SVM Based Identification of Psychological Personality Using Handwritten Text IJERA Editor
Identification of Personality is a complex process. To ease this process, a model is developed using cursive
handwriting. Area based, width based and height based thresholds are set for only character selection, word
selection and line selection. The rest is considered as noise. Followed by feature vector construction. Slope
feature using slope calculation, shape features and edge detection done using Sobel filter and direction
histogram is considered. Based on the direction of handwriting the analysis was done. Writing which rises to
the right shows optimism and cheerfulness. Sagging to the right shows physical or mental weariness. The lines
which are straight, reveals over-control to compensate for an inner fear of loss of control.The analysis was done
using single line and multiple lines. Simple techniques have provided good results. The results using single line
were 95% and multiple lines were 91%.The classification is done using SVM classifier.
The Geometric Characteristics of the Linear Features in Close Range Photogram...IJERD Editor
The accuracy of photogrammetry can be increased with better instruments, careful geometric
characteristics of the system, more observations and rigorous adjustment. The main objective of this research is
to develop a new mathematical model of two types of linear features (straight line, spline curve) in addition to
relating linear features in object space to the image space using the Direct Linear Transformation (DLT). The
second main objective of the present paper is to study of some geometric characteristics of the system, when the
linear features are used in close range photogrammetric reduction processes. In this research, the accuracy
improvement has been evaluated by adopting certain assessment criteria, this will be performed by computing
the positional discrepancies between the photogrammetrically calculated object space coordinates of some check
object points, with the original check points of the test field, in terms of their respective RMS errors values. In
addition, the resulting least squares estimated covariance matrices of the check object point's space coordinates.
To perform the above purposes, some experiments are performed with synthetic images. The obtained results
showed significant improvements in the positional accuracy of close range photogrammetry, when starting node,
end nodes, and interior node on straight line and spline curve are increased with certain specifications regarding
the location and magnitude of each type of them.
The aspect sensitivity of high-resolution range profile (HRRP) leads to the anomalous change of the HRRP statistical characteristic, which is one of inextricable problems on the target recognition based on HRRP. Aiming at the HRRP statistical characteristic, an adaptive angular-sector segmentation method is proposed through based on the grey relational mode. Comparing to the equal interval angular-sector segmentation method, the new method improves the recognition performance. And these simulation results of five kinds of aircraft targets HRRPs prove the feasibility and validity.
PERFORMANCE ASSESSMENT OF CHAOTIC SEQUENCE DERIVED FROM BIFURCATION DEPENDENT...IJCNCJournal
In CDMA system, m-sequence and Gold codes are often utilized for spreading-despreading and
scrambling-descrambling operations. In a previous work, a design framework was created for generating
large family of codes from logistic map, which have comparable autocorrelation and cross correlation to
m-sequence and Gold codes. The purpose of this work is to evaluate the performance of these chaotic
codes in a CDMA environment. In the bit error rate (BER) simulation, matched filter, decorrelator and
MMSE receiver have been utilized. The received signal was modelled for synchronous CDMA uplink for
simulation simplicity purpose. Additive White Gaussian Noise channel model was assumed for the
simulation.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
2. Fingerprint ClassificationFingerprint ClassificationFingerprint classification is a coarse level partitioning of a fingerprint database into smaller subsets. Fingerprint classification reduces the search space of a large database: Determine the class of the query fingerprint. Then, only search templates with the same class as the query. Illustration: Inputs are the fingerprint impressions from 10 fingers of an individual. If size of the database is N and D is the number of classes,
Search space without classification: N10
Search space with classification: (N/D)10
3. The Henry Classification SystemThe System•Henry (1900) made an extensive study of occurrence of fingerprints and indexed them into 8 major classes. •The 8 classes are shown above. The four different whorl classes can be combined into one class: Whorl (W).
4. The Henry Classification System (cont.)Left-loop (LL)Right-loop (RL)Whorl (W)
Tented Arch (TA)
Plain Arch (PA)
The Henry system with five classes are shown above. The fiveclasses can be reduced to four by combining the PA and TA classes to form the Arch (A) class. The natural frequencies of W, L, R and A (A + T) are 27.9%, 33.8%, 31.7% and 6.6%.
5. The Henry Classification System (cont.)•The five main classes differ in terms of the global flow patterns of the ridge curves. •They also differ in terms of the number and locations of singular points in the fingerprint image. For example, ¾LL –exactly one core and one delta; the core is to the left of the delta, ¾RL –exactly one core and one delta; the core is to the right of thedelta, ¾W –two cores and two deltas, ¾PA –no singular points, and ¾TA –one core and one delta; the delta is approximately directly below the core. Problems with the Henry classification system: (i) non- uniform classification proportions, and (ii) experts classify
some fingerprint images into different Henry classes.
7. Approaches for Fingerprint ClassificationApproaches ClassificationApproaches based on singular points: Hong and Jain (1999), Karu and Jain (1996). Structure-basedapproaches such as using the orientation field and geometry of ridges: Cappelli et. al (2002), Chang & Fan (2002), and Chong et. al (1997). Frequency-basedapproaches using Fourier spectrum: Jain et. al (1999). Syntactic or grammar-based: Moayer & Fu (1975,1976). Mathematical models: Silviu & Jain (2002), Dass & Jain (2004). Hybrid methods: Combination of at least two of the above approaches (Chang & Fan (2002) and Chong et. al (1997))
8. Singular point based approaches
Input imageOrientation FieldKaruand Jain (1996) classifies fingerprints by detectingsingular points in the fingerprint image. 1.The orientation field (flow direction of the ridges at each sitein the fingerprint image) is extracted and smoothed. 2.Singular points are detected using the Poincareindex. The Poincareindex is computed by summing the changes in the angles of flow in a small circle around the test point. It is 0, -π, π, and 2πfor regular, delta, core and double core points, respectively. CoreDelta
9. Karu and Jain (1996), cont.
The classification procedure is:
Get singular pointsDetermine the number of core-delta pairs, NArchLoop or tented arch ? 012Left-loopRight-loopTented ArchWhorlTwin Loop1.If N=1, consider the straight line joining the core and the delta. If N=2, consider the straight line joining the two cores. Call this lineL. 2.For tented arch (whorl), the tangent direction of L is parallel to the local orientation values, but not so for loops (twin loop).
10. Structure based approaches
Structure based approaches use global characteristics of the ridges to determine the fingerprint class.
Chang & Fan (2002) use ridge distribution models to determine the class of the fingerprint.
The 10 basic ridge patterns of Chang & Fan are given on the left.
11. Chang & Fan (2002)
The fingerprint classification procedure is based on the following steps:
1.For a given fingerprint image, an algorithm for extracting theridges is developed. This algorithm takes into account (i) ridge bifurcations, and (ii) ridge fragmentations which are not endings.
(i) Handling ridge bifurcations
(i) Handling ridge fragmentations (a), true ridge endings (b).
12. Chang & Fan (2002), cont.
2. Each extracted ridge is then classified into one of the 10 basic ridge patterns. Some examples of the classification are:
13. Chang & Fan (2002), cont.
3. The ridge distribution sequence is generated according to the picture below: ¾Each of the 7 classes of the Henry system (except the accidentalwhorl) has a unique ridge distribution sequence associated to it. ¾Fingerprint images whose ridge distribution sequence cannot be determined are rejected. ¾The accidental whorl class is a subset of the rejected images. ¾Experimental results with the NIST4 database: 93.4% with 5.1% rejection rate for 7 classes, and 94.4% for the 5 classes.
14. Structural based approach - Chong & Ngee (1997)
1.The fingerprint classification procedure is based on determiningthe global geometric structure of the extracted ridges using B- splines. 2.The B-splinesprovide a compact representation of the ridges and contain enough information to determine their geometric structure. 3.The main drawback of this method is that it was not tested on a large number of fingerprints. Frequency based method ––Jain et. al (1999)1.Frequency based approached covert the fingerprint image into thefrequency space and perform the classification in that space. 2.In Jain et. al (1999), Gaborfilters at 16 different orientation values are applied to different sectors of the fingerprint image. The Gaborcoefficients form the feature for classification.
16. Fingerprint Classification Algorithm•A. K. Jain, S. Prabhakarand L. Hong, "A MultichannelApproach to Fingerprint Classification",IEEE Transactions on PAMI, Vol.21, No.4, pp. 348-359, April 1999.
19. •Five-class classification error is 10%; error is 4% with 30.8% rejection rate. •Four-class classification error is 5.2%; error is 2.2% with 30.8% rejection rate. WhorlRight LoopLeft LoopArchTented ArchWhorl36616841Right Loop33721817Left Loop6036467Arch21340539Tented Arch061455261Assigned ClassTrue Class0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 5-class Error Rate 0%7%18%31% Reject RateClassification Results
20. Approaches based on mathematical models ––
Silviu & Jain, 2002
Class-specific kernels are defined: the kernel for the whorl class is the unit circle, and for the other classes, the kernelsare defined via splines. Figure: Kernels for (a) arch, (b) left-loop, (c) right-loop, and (d) whorl. (a)(b)(c)(d)
For a fingerprint image, the energy functionalis minimized to determine the best fitting kernel.
21. Silviu & Jain (cont.)
Results of the fitting algorithm: The best fitting kernel (the one that minimizes the energy functional below a certain threshold) is taken to be the class of the fingerprint image. Experimental results based on the NIST4 database yields a classification accuracy of 91.25% for the 4 class problem.
22. Why Another Fingerprint Classifier ?Dass & Jain (2004)Limitations of the existing approaches:Ridges are subject to breaks and discontinuities due to noise Singular points may be missed in some fingerprint imagesMathematical models are too rigid to represent all possible ridge variationsRequirements of a fingerprint classifier:Requirements Robust detection of global ridge characteristicsRobust characteristicsClassification Classification invariance under affine transformationsinvariance transformationsand and mild nonmild non--linear deformations of the fingerprintlinear fingerprint
23. Orientation Field Flow Curves
Fingerprint Image
Orientation Field
Flow Curves
•Orientation field: local flow directions of the ridges and valleys •Opposite flow directions are equivalent, angle ε[-π/2, π/2] •Orientation field flow curve(OFFC) is a curve whose tangent direction at each point is parallel to the orientation field direction•This is not ridge tracing; OFFCsare pseudoridges
24. Schematic Diagram of ClassificationSchematic ClassificationInput FingerprintFingerprint class A, L, R, or WDetermine fingerprint classEstimate Orientation FieldGenerate OFFCsDetermine labels of OFFCs
25. Estimation of Orientation Field Field*
Input imageOrientation FieldA block-wise squared gradient approachwith smoothness prioris used to obtain a smooth and robust estimate of orientation field*S. C. Dass, “Markov Random Field Models for Directional Field and Singularity Extraction in Fingerprint Images”, IEEE Transactionson Image Processing, October 2004
26. Generation of Orientation Field Flow
Curves ( OFFCs OFFCs)
Continuous Orientation Field
From a starting point s0, an OFFC is generated by tracing the paths from s0that is tangential to orientation fieldOrientation FieldFlow Curves
27. Detecting OFFC Type using Tangent
Space Isometric Maps
For each OFFC, we wish to determine whether it is a loop, arch or whorlThe curve type can be identified using the tangent space isometric map of the OFFCDenote one end of the OFFC by se. Obtain the tangent plane at se, TeFor an intermediate point s on the OFFC, obtain the tangent plane at s, TsRotate Teto match Ts; say, the angle of rotation is θsThe tangent space isometric map is the plot of cos(θs) versus ds, the distance of s from sealong the OFFC
28. Tangent Space Maps of LeftTangent Left--LoopLoop102030405060708090100−1−0.8−0.6−0.4−0.200.20.40.60.81Isometric map plotj cos γ 102030405060708090100−1−0.8−0.6−0.4−0.200.20.40.60.81Isometric map plotj cos γ
32. Tangent Space Isometries of OFFCsTangent OFFCsThe number of zero crossings, and values of local maxima and minimabetween zero crossings are the salient featuresLeft-and right-loops are differentiated based on sign changes Ux* Uyof the tangent vector (Ux,Uy) Left-loops are characterized by sign transitions of from +1 to -1 and back to +1. Right-loops are characterized by sign transitions of from -1 to +1 and back to -1Note that these features are invariant to rotation, translation and scale
33. Fingerprint Classification Rules
Classify each OFFC as whorl, left-loop, right-loop or arch using the tangent space isometric mapsLet Nw, Nl, Nrand Nadenote the number of OFFCsclassified as whorl, left-loop, right-loop and archSelect thresholds λw, λl, and λr. The classification rule isIf Nw> λw, classify as Whorl; Else: If Nl> λland Nr≤ λr, classify as Left-loop; If Nl≤ λland Nr> λr, classify as Right-loop; If Nl≤ λl and Nr≤ λr, classify as Arch
34. Classification ResultsClassification ResultsExperiments were conducted on the NIST 4 fingerprint database containing 4,000 8-bit gray scale fingerprint images Select λw= 2, λl= 2 and λr= 1Classification into 4 classes yielded an accuracy of 94.4% Assigned ClassTrue classALRW100
L 63 7301680091.2%
R 75 4720180090.0%
W 12 231874780093.3% 0219797781ATtotalaccuracy80099.6% 80097.2%
36. Sources of Classification Errors
Oversmoothingof the orientation field
True class: L; Assigned class: A
37. Detection of spurious loopsTrue class: A; Assigned class: L
Sources of Classification Errors
38. Summary and Future WorkSummary WorkWe have proposed a fingerprint classification scheme based on the flow curves derived from the orientation fieldPerformance of the proposed approach is comparable with the other state-of-the-art methodsWe plan to extend the 4-class classification to the 5-and 7- class problems by including other features of OFFCs into our classification procedureOther indexing techniques, besides the Henry system, will be investigated based on relevant features of the OFFCs