Geppy Parziale

A fingerprint capture consists of touching or rolling a finger onto a rigid sensing surface. During this act, the elastic skin of the finger deforms. The quantity and direction of the pressure applied by the user, the skin conditions, and the projection of an irregular 3D object (the finger) onto a 2D flat plane introduce distortions, noise, and inconsistencies on the captured fingerprint image. Due to these negative effects, the representation of the same fingerprint changes every time the… Show more

A fingerprint capture consists of touching or rolling a finger onto a rigid sensing surface. During this act, the elastic skin of the finger deforms. The quantity and direction of the pressure applied by the user, the skin conditions, and the projection of an irregular 3D object (the finger) onto a 2D flat plane introduce distortions, noise, and inconsistencies on the captured fingerprint image. Due to these negative effects, the representation of the same fingerprint changes every time the finger is placed on the sensor platen, increasing the complexity of the fingerprint matching and representing a negative influence on the system performance. Recently, a new approach to capture fingerprints has been proposed. This approach, referred to as touchless or contactless fingerprinting, tries to overcome the above-cited problems. Because of the lack of contact between the finger and any rigid surface, the skin does not deform during the capture and the repeatability of the measure is quiet ensured. However, this technology introduces new challenges. Finger positioning, illumination, image contrast adjustment, data format compatibility, and user convenience are key in the design and development of touchless fingerprint systems. In addition, vulnerability to spoofing attacks of some touchless fingerprint systems must be addressed. Show less

Biometrics refers to automated methods of recognizing a person based on physiological or behavioral characteristics. The Encyclopedia of Biometrics provides a comprehensive reference to topics in Biometrics, including concepts, modalities, algorithms, devices, systems, security, performance testing, applications and standardization.

With an A–Z format, the Encyclopedia of Biometrics provides easy access to relevant information on all aspects of biometrics for those seeking entry into… Show more

Biometrics refers to automated methods of recognizing a person based on physiological or behavioral characteristics. The Encyclopedia of Biometrics provides a comprehensive reference to topics in Biometrics, including concepts, modalities, algorithms, devices, systems, security, performance testing, applications and standardization.

With an A–Z format, the Encyclopedia of Biometrics provides easy access to relevant information on all aspects of biometrics for those seeking entry into this broad field. The Encyclopedia is composed of approximately 250 overview entries, and 800 definitional entries.

Each entry includes a definition, key words, list of synonyms, list of related entries, illustration(s), applications, and a bibliography. Most entries include useful literature references providing the reader with a portal to more detailed information. The style of the entries is expository and tutorial, making the encyclopedia a practical resource for experts in the field and professionals in other fields. Show less

Fingerprint image acquisition is considered the most critical step of an automated fingerprint authentication system, as it determines the final fingerprint image quality, which has drastic effects on the overall system performance.
When a finger touches or rolls onto a surface, the elastic skin deforms. The quantity and direction of the pressure applied by the user, the skin conditions, and the projection of an irregular 3D object (the finger) onto a 2D flat plane introduce distortions… Show more

Fingerprint image acquisition is considered the most critical step of an automated fingerprint authentication system, as it determines the final fingerprint image quality, which has drastic effects on the overall system performance.
When a finger touches or rolls onto a surface, the elastic skin deforms. The quantity and direction of the pressure applied by the user, the skin conditions, and the projection of an irregular 3D object (the finger) onto a 2D flat plane introduce distortions, noise, and inconsistencies on the captured fingerprint image. These problems have been indicated as inconsistent, irreproducible, and nonuniform contacts and, during each acquisition, their effect on the same fingerprint is different and uncontrollable. Hence, the representation of the same fingerprint changes every time the finger is placed on the sensor platen, increasing the complexity of fingerprint matching and representing a negative influence on system performance with a consequent limited spread of this biometric technology.
Recently, a new approach to capture fingerprints has been proposed. This approach, referred to as touchless or contactless fingerprinting, tries to overcome the above-cited problems. Because of the lack of contact between the finger and any rigid surface, the skin does not deform during the capture and the repeatability of the measure is ensured.
However, this technology introduces new challenges. For example, due to the curvature of the finger and the nonnull distance between the camera and the finger, the useful captured fingerprint area is reduced and the capture of rolled-equivalent fingerprints becomes very difficult. Moreover, finger positioning, lower image contrast, illumination, and user convenience still must be addressed. Show less

Fingerprints are traditionally captured based on contact of the finger on paper or a platen surface. This often results in partial or degraded images due to improper finger placement, skin deformation, slippage and smearing, or sensor noise from wear and tear of surface coatings. A new generation of touchless live scan devices that generate 3D representation of fingerprints is appearing in the market. This new sensing technology addresses many of the problems stated above. However, 3D touchless… Show more

Fingerprints are traditionally captured based on contact of the finger on paper or a platen surface. This often results in partial or degraded images due to improper finger placement, skin deformation, slippage and smearing, or sensor noise from wear and tear of surface coatings. A new generation of touchless live scan devices that generate 3D representation of fingerprints is appearing in the market. This new sensing technology addresses many of the problems stated above. However, 3D touchless fingerprint images need to be compatible with the legacy rolled images used in automated fingerprint identification systems (AFIS). In order to solve this interoperability issue, we propose an unwrapping algorithm that unfolds the 3D fingerprint in such a way that it resembles the effect of virtually rolling the 3D finger on a 2D plane. Our preliminary experiments show promising results in obtaining touchless fingerprint images that are of high quality and at the same time compatible with legacy rolled fingerprint images. Show less

A novel solution for automatic hardwood inspection is presented. A sophisticated multi sensor system is required for reliable results. Our system works on a data stream of more than 50 MByte/Sec in input and up to 100 MByte/Sec inside the processing queue. The algorithm is divided into multiple steps. Along a fixed grid the images are decomposed into small squares. 55 texture- and color features are computed for each square. A Maximum Likelihood classifier assigns each square to one out of 12… Show more

A novel solution for automatic hardwood inspection is presented. A sophisticated multi sensor system is required for reliable results. Our system works on a data stream of more than 50 MByte/Sec in input and up to 100 MByte/Sec inside the processing queue. The algorithm is divided into multiple steps. Along a fixed grid the images are decomposed into small squares. 55 texture- and color features are computed for each square. A Maximum Likelihood classifier assigns each square to one out of 12 defect classes with a recognition rate better than 97%. Depending on the defect type a dedicated threshold operation is performed for segmentation. Threshold levels and the selection of the input channel (RGB + filtered images) is the result of the former classification step. A fast algorithm computes bounding rectangles from blobs. Defect type dependent rules are used to combine rectangles. Two additional fast high resolution 3D measurement systems add board shape and 3D defect information. All defect rectangles are passing an additional plausibility check in the last data fusion process before they are delivered to the optimization computer. To guarantee a short response time, image acquisition and image processing are performed in parallel on parallel computing hardware. Show less

The Surround ImagerTM, an innovative multi-camera touchless device able to capture rolled-equivalent fingerprints, is here presented for the first time. Due to the lack of contact between the elastic skin of the finger and any rigid surface, the acquired images present no deformation. The multi-camera system acquires different finger views that are combined together to provide a 3D representation of the fingerprint. This new representation leads to a new definition of minutiae bringing new… Show more

The Surround ImagerTM, an innovative multi-camera touchless device able to capture rolled-equivalent fingerprints, is here presented for the first time. Due to the lack of contact between the elastic skin of the finger and any rigid surface, the acquired images present no deformation. The multi-camera system acquires different finger views that are combined together to provide a 3D representation of the fingerprint. This new representation leads to a new definition of minutiae bringing new challenges in the field of fingerprint recognition. Show less

We present a new technique for fingerprint minutiae matching. The proposed method connects minutiae using a Delaunay triangulation and analyzes the relative position and orientation of each minutia with respect to its neighbors obtained by the triangle structure. Due to non-linear deformations, we admit a certain degree of triangle deformation. If rotations and translations are present, the triangle structure does not change consistently. Two fingerprints are considered matching, if their… Show more

We present a new technique for fingerprint minutiae matching. The proposed method connects minutiae using a Delaunay triangulation and analyzes the relative position and orientation of each minutia with respect to its neighbors obtained by the triangle structure. Due to non-linear deformations, we admit a certain degree of triangle deformation. If rotations and translations are present, the triangle structure does not change consistently. Two fingerprints are considered matching, if their triangle structures are similar according the neighbor relationship. The algorithm performance are evaluated on a public domain database. Show less

To abruptly reduce the production costs of fingerprint systems and push up their wide-spreading, some chip-makers introduced on the market new kind of fingerprint sensors. They are charac- terized by their reduced dimensions and a special procedure to acquire the fingerprint, since the finger has to sweep over the sensing area. During the finger movement, the device generates a certain number of frames representing small portions of the ridge-valley pattern. We present an algorithm for the… Show more

To abruptly reduce the production costs of fingerprint systems and push up their wide-spreading, some chip-makers introduced on the market new kind of fingerprint sensors. They are charac- terized by their reduced dimensions and a special procedure to acquire the fingerprint, since the finger has to sweep over the sensing area. During the finger movement, the device generates a certain number of frames representing small portions of the ridge-valley pattern. We present an algorithm for the reconstruction of the fingerprints acquired with these sensors. Moreover, we show that is possible to extract minutiae on-the-fly, without the need to store the whole frame sequence. Show less

Multimedia information systems become always more important due to the new compres- sion standards, the increase of wide-spreading of broadband networks and the growing computational power. Thus, numerous applications (telemedicine, interactive television, digital photo libraries, geographical information systems, home entertainment) heavily rely on visual media. Because of the required computational power and the large amount of memory space, image databases often need efficient indexing and… Show more

Multimedia information systems become always more important due to the new compres- sion standards, the increase of wide-spreading of broadband networks and the growing computational power. Thus, numerous applications (telemedicine, interactive television, digital photo libraries, geographical information systems, home entertainment) heavily rely on visual media. Because of the required computational power and the large amount of memory space, image databases often need efficient indexing and searching mecha- nisms. New automatic image indexing techniques have been proposed. These methods extract image features, like color, object orientation, shape, edges. In this work, a com- parative study of four known techniques based on global image features is presented to evaluate their retrieval ability. Show less

The increasing demand for wood products with better defined quality parameters has led the wood industry to find improved methods for converting logs into final products. The number and the type of defects (e.g. knots, cracks, resin pockets, wane, pith, and decay) define the quality of lumber. In particular, the presence or absence of these defects determines the visual appearance, constructive strength and, in combination, the quality and price of the product. Computer Tomography (CT) can be… Show more

The increasing demand for wood products with better defined quality parameters has led the wood industry to find improved methods for converting logs into final products. The number and the type of defects (e.g. knots, cracks, resin pockets, wane, pith, and decay) define the quality of lumber. In particular, the presence or absence of these defects determines the visual appearance, constructive strength and, in combination, the quality and price of the product. Computer Tomography (CT) can be used to inspect the internal structure of logs and to determine their interior defects before breakdown. In this paper, we present a method for the localization of resin pockets in CT images, based on anisotropic diffusion filtering. We use this filter to enhance the resin pockets edges, afterwards detected by an edge detector. Show less