Segmentation of Fingerprint Image Using Block-Wise Coherence Algorithm

The Segmentation of fingerprint image is an important step in the fingerprint identification. The objective of the fingerprint image segmentation is to separating the foreground regions from the background regions. Accurate segmentation of fingerprint images influences directly the performance of minutiae extraction like minutiae and singular points. In this paper, an algorithm for the segmentation of fingerprint image is presented. The method uses block-wise coherence. Fingerprint data has been taken from NIST databases 14. The segmentation algorithm has been trained on fingerprints of this database, but not on these particular fingerprints. Human inspection shows that the block-wise coherence algorithm provides satisfactory result

Segmentation Of Fingerprint Image Using Block-Wise Coherence Algorithm

The Segmentation of fingerprint image is an important step in the fingerprint identification. The objective of the fingerprint image segmentation is to separating the foreground regions from the background regions. Accurate segmentation of fingerprint images influences directly the performance of minutiae extraction like minutiae and singular points. In this paper, an algorithm for the segmentation of fingerprint image is presented. The method uses block-wise coherence. Fingerprint data has been taken from NIST databases 14. The segmentation algorithm has been trained on fingerprints of this database, but not on these particular fingerprints. Human inspection shows that the block-wise coherence algorithm provides satisfactory result. Keyword: fingerprint image segmentation, block-wise, coherence, minutiae, singular point

Segmentation of Fingerprint Image Based on Gradient Magnitude and Coherence

Fingerprint image segmentation is an important pre-processing step in automatic fingerprint recognition system. A well-designed fingerprint segmentation technique can improve the accuracy in collecting clear fingerprint area and mark noise areas. The traditional grey variance segmentation method is widely and easily used, but it can hardly segment fingerprints with low contrast of high noise. To overcome the low image contrast, combining two-block feature; mean of gradient magnitude and coherence, where the fingerprint image is segmented into background, foreground or noisy regions,  has been done. Except for the noisy regions in the foreground, there are still such noises existed in the background whose coherences are low, and are mistakenly assigned as foreground. A novel segmentation method based on combination local mean of grey-scale and local variance of gradient magnitude is presented in this paper. The proposed extraction begins with normalization of the fingerprint. Then, it is followed by foreground region separation from the background. Finally, the gradient coherence approach is used to detect the noise regions existed in the foreground. Experimental results on NIST-Database14 fingerprint images indicate that the proposed method gives the impressive results

-Means Based Fingerprint Segmentation with Sensor Interoperability

A critical step in an automatic fingerprint recognition system is the segmentation of fingerprint images. Existing methods are usually designed to segment fingerprint images originated from a certain sensor. Thus their performances are significantly affected when dealing with fingerprints collected by different sensors. This work studies the sensor interoperability of fingerprint segmentation algorithms, which refers to the algorithm's ability to adapt to the raw fingerprints obtained from different sensors. We empirically analyze the sensor interoperability problem, and effectively address the issue by proposing a k-means based segmentation method called SKI. SKI clusters foreground and background blocks of a fingerprint image based on the k-means algorithm, where a fingerprint block is represented by a 3-dimensional feature vector consisting of block-wise coherence, mean, and variance (abbreviated as CMV). SKI also employs morphological postprocessing to achieve favorable segmentation results. We perform SKI on each fingerprint to ensure sensor interoperability. The interoperability and robustness of our method are validated by experiments performed on a number of fingerprint databases which are obtained from various sensors

Two-Level Evaluation on Sensor Interoperability of Features in Fingerprint Image Segmentation

Features used in fingerprint segmentation significantly affect the segmentation performance. Various features exhibit different discriminating abilities on fingerprint images derived from different sensors. One feature which has better discriminating ability on images derived from a certain sensor may not adapt to segment images derived from other sensors. This degrades the segmentation performance. This paper empirically analyzes the sensor interoperability problem of segmentation feature, which refers to the feature’s ability to adapt to the raw fingerprints captured by different sensors. To address this issue, this paper presents a two-level feature evaluation method, including the first level feature evaluation based on segmentation error rate and the second level feature evaluation based on decision tree. The proposed method is performed on a number of fingerprint databases which are obtained from various sensors. Experimental results show that the proposed method can effectively evaluate the sensor interoperability of features, and the features with good evaluation results acquire better segmentation accuracies of images originating from different sensors

Surface Modeling and Analysis Using Range Images: Smoothing, Registration, Integration, and Segmentation

This dissertation presents a framework for 3D reconstruction and scene analysis, using a set of range images. The motivation for developing this framework came from the needs to reconstruct the surfaces of small mechanical parts in reverse engineering tasks, build a virtual environment of indoor and outdoor scenes, and understand 3D images. The input of the framework is a set of range images of an object or a scene captured by range scanners. The output is a triangulated surface that can be segmented into meaningful parts. A textured surface can be reconstructed if color images are provided. The framework consists of surface smoothing, registration, integration, and segmentation. Surface smoothing eliminates the noise present in raw measurements from range scanners. This research proposes area-decreasing flow that is theoretically identical to the mean curvature flow. Using area-decreasing flow, there is no need to estimate the curvature value and an optimal step size of the flow can be obtained. Crease edges and sharp corners are preserved by an adaptive scheme. Surface registration aligns measurements from different viewpoints in a common coordinate system. This research proposes a new surface representation scheme named point fingerprint. Surfaces are registered by finding corresponding point pairs in an overlapping region based on fingerprint comparison. Surface integration merges registered surface patches into a whole surface. This research employs an implicit surface-based integration technique. The proposed algorithm can generate watertight models by space carving or filling the holes based on volumetric interpolation. Textures from different views are integrated inside a volumetric grid. Surface segmentation is useful to decompose CAD models in reverse engineering tasks and help object recognition in a 3D scene. This research proposes a watershed-based surface mesh segmentation approach. The new algorithm accurately segments the plateaus by geodesic erosion using fast marching method. The performance of the framework is presented using both synthetic and real world data from different range scanners. The dissertation concludes by summarizing the development of the framework and then suggests future research topics

Comparing Features of Three-Dimensional Object Models Using Registration Based on Surface Curvature Signatures

This dissertation presents a technique for comparing local shape properties for similar three-dimensional objects represented by meshes. Our novel shape representation, the curvature map, describes shape as a function of surface curvature in the region around a point. A multi-pass approach is applied to the curvature map to detect features at different scales. The feature detection step does not require user input or parameter tuning. We use features ordered by strength, the similarity of pairs of features, and pruning based on geometric consistency to efficiently determine key corresponding locations on the objects. For genus zero objects, the corresponding locations are used to generate a consistent spherical parameterization that defines the point-to-point correspondence used for the final shape comparison

Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 2

Papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake, held 1-3 Jun. 1992 at the Lyndon B. Johnson Space Center in Houston, Texas are included. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making

Biometric security: A novel ear recognition approach using a 3D morphable ear model

Biometrics is a critical component of cybersecurity that identifies persons by verifying their behavioral and physical traits. In biometric-based authentication, each individual can be correctly recognized based on their intrinsic behavioral or physical features, such as face, fingerprint, iris, and ears. This work proposes a novel approach for human identification using 3D ear images. Usually, in conventional methods, the probe image is registered with each gallery image using computational heavy registration algorithms, making it practically infeasible due to the time-consuming recognition process. Therefore, this work proposes a recognition pipeline that reduces the one-to-one registration between probe and gallery. First, a deep learning-based algorithm is used for ear detection in 3D side face images. Second, a statistical ear model known as a 3D morphable ear model (3DMEM), was constructed to use as a feature extractor from the detected ear images. Finally, a novel recognition algorithm named you morph once (YMO) is proposed for human recognition that reduces the computational time by eliminating one-to-one registration between probe and gallery, which only calculates the distance between the parameters stored in the gallery and the probe. The experimental results show the significance of the proposed method for a real-time application

3D minutiae extraction in 3D fingerprint scans.

Traditionally, fingerprint image acquisition was based on contact. However the conventional touch-based fingerprint acquisition introduces some problems such as distortions and deformations to the fingerprint image. The most recent technology for fingerprint acquisition is touchless or 3D live scans introducing higher quality fingerprint scans. However, there is a need to develop new algorithms to match 3D fingerprints. In this dissertation, a novel methodology is proposed to extract minutiae in the 3D fingerprint scans. The output can be used for 3D fingerprint matching. The proposed method is based on curvature analysis of the surface. The method used to extract minutiae includes the following steps: smoothing; computing the principal curvature; ridges and ravines detection and tracing; cleaning and connecting ridges and ravines; and minutiae detection. First, the ridges and ravines are detected using curvature tensors. Then, ridges and ravines are traced. Post-processing is performed to obtain clean and connected ridges and ravines based on fingerprint pattern. Finally, minutiae are detected using a graph theory concept. A quality map is also introduced for 3D fingerprint scans. Since a degraded area may occur during the scanning process, especially at the edge of the fingerprint, it is critical to be able to determine these areas. Spurious minutiae can be filtered out after applying the quality map. The algorithm is applied to the 3D fingerprint database and the result is very encouraging. To the best of our knowledge, this is the first minutiae extraction methodology proposed for 3D fingerprint scans