Penerapan Multi-threading untuk Meningkatkan Kinerja Pengolahan Citra Digital

Penelitian ini menggunakan teknik multi-threading dalam pengolahan citra digital untuk menghasilkan pengolahan citra digital yang lebih cepat. Selain itu, penelitian ini memperbandingkan kecepatan akurasi antara single-threading dengan multi-threading. Hasil pengujian menggunakan teknik multi-threading memperlihatkan waktu proses semakin bertambah cepat, bila jumlah sampel bertambah banyak. Hal ini menunjukkan bahwa teknik multi-threading memiliki waktu proses yang optimal dalam pengolahan citra digital dibandingkan dengan single-threading

Penerapan Multi-threading untuk Meningkatkan Kinerja Pengolahan Citra Digital

Penelitian ini menggunakan teknik multi-threading dalam pengolahan citra digital untuk menghasilkan pengolahan citra digital yang lebih cepat. Selain itu, penelitian ini memperbandingkan kecepatan akurasi antara single-threading dengan multi-threading. Hasil  pengujian  menggunakan  teknik  multi-threading  memperlihatkan  waktu  proses semakin bertambah cepat, bila jumlah sampel bertambah banyak. Hal ini menunjukkan bahwa  teknik  multi-threading  memiliki  waktu  proses  yang  optimal  dalam  pengolahan citra digital dibandingkan dengan single-threading

An Efficient Fingerprint Identification using Neural Network and BAT Algorithm

The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entropy, and SIFT features are extracted. The final phase is classification using hybrid Neural for classification of fingerprint as fake or original. The neural network is unified with BAT algorithm for optimizing the weight factor

Fingerprint fuzzy vault: Security analysis and a new scheme

A fingerprint fuzzy vault uses a fingerprint A to lock a strong secret k and only a close fingerprint from the same finger can be used to unlock k. An attacker who has stolen the vault will not be able to get useful information about A or k.%0d%0a In this research, we shall study the security of a major fingerprint fuzzy vault developed by Nandakumar et al. through investigating the security implication of helper data, which are stored in the fuzzy vault for fingerprint alignment. We will show that helper data leak information about fingerprints and thus compromise the security claim on the fingerprint fuzzy vault scheme. Next, we will propose a new fingerprint fuzzy vault scheme, which is based on traditional representation of fingerprints in minutia points and does not need helper data for alignment

Desarrollo de un algoritmo de clasificación de la huella dactilar para la Policía Nacional del Perú

Las huellas digitales son únicas en cada ser humano, con este razonamiento y sus diferentes características las cuales permiten que puedan ser clasificadas, el ser humano las está usando para poder identificar a las personas es uno de los métodos de reconocimiento más confiables hoy en día. En nuestra realidad, la Dirección de Criminalística (DIRCRI) de la Policía Nacional del Perú usa las impresiones de las huellas las cuales son clasificadas según la forma de las crestas papilares características de cada individuo. Dado que en la actualidad la clasificación requiere de un proceso visual y el almacenamiento en tarjetas dactiloscópicas genera que estas no tengan un control de cantidad se busca que el proceso de clasificación lo haga más rápido y eficiente. Este documento de tesis ha sido desarrollado en 4 capítulos: En el primer capítulo hace referencia a sistemas de adquisición de la huella digital, aplicación en la policía y el marco problemático. En el segundo capítulo se describirá los usos de las huellas dactilares así como también se explican las cualidades, características y diferentes formas de clasificación. En el tercer capítulo se encuentra la estrategia del desarrollo del algoritmo para llevar a cabo esta tesis. En el último capítulo se mencionan los resultados obtenidos y comentarios para una mejor adquisición. Finalmente se concluye que es primordial tener una buena impresión para obtener un óptimo resultado en la clasificación, las impresiones más nítidas han sido satisfactoriamente clasificadas mientras que las que omiten información presentaron problemas al momento de la evaluación.Tesi

Fuzzy vault scheme for fingerprint verification: implementation, analysis and improvements

Fuzzy vault is a well-known technique that is used in biometric authentication applications. This thesis handles the fuzzy vault scheme and improves it to strengthen against previously suggested attacks while analyzing the effects of these improvements on the performance. We compare the performances of two different methods used in the implementation of fuzzy vault, namely brute force and Reed Solomon decoding with fingerprint biometric data. We show that the locations of fake (chaff) points leak some valuable information and propose a new chaff point placement technique that prevents that information leakage. A novel method for chaff point creation that decreases the success rate of the brute force attack from 100% to less than 3.3% is also proposed in this work. Moreover, a special hash function that allows us to perform matching in the hash space which protects the biometric information against the 'correlation attack' is proposed. Security analysis of this method is also presented in this thesis. We implemented the scheme with and without the hash function to calculate false accept and false reject rates in different settings

Error propagation in pattern recognition systems: Impact of quality on fingerprint categorization

The aspect of quality in pattern classification has recently been explored in the context of biometric identification and authentication systems. The results presented in the literature indicate that incorporating information about quality of the input pattern leads to improved classification performance. The quality itself, however, can be defined in a number of ways, and its role in the various stages of pattern classification is often ambiguous or ad hoc. In this dissertation a more systematic approach to the incorporation of localized quality metrics into the pattern recognition process is developed for the specific task of fingerprint categorization. Quality is defined not as an intrinsic property of the image, but rather in terms of a set of defects introduced to it. A number of fingerprint images have been examined and the important quality defects have been identified and modeled in a mathematically tractable way. The models are flexible and can be used to generate synthetic images that can facilitate algorithm development and large scale, less time consuming performance testing. The effect of quality defects on various stages of the fingerprint recognition process are examined both analytically and empirically. For these defect models, it is shown that the uncertainty of parameter estimates, i.e. extracted fingerprint features, is the key quantity that can be calculated and propagated forward through the stages of the fingerprint classification process. Modified image processing techniques that explicitly utilize local quality metrics in the extraction of features useful in fingerprint classification, such as ridge orientation flow field, are presented and their performance is investigated

Fingerprint classification using orientation field flow curves

Manual fingerprint classification proceeds by carefully inspecting the geometric characteristics of major ridge curves in a fingerprint image. We propose an automatic approach of identifying the geometric characteristics of ridges based on curves generated by the orientation field called orientation field flow curves (OFFCs). The geometric characteristics of OFFCs are analyzed by studying the isometric maps of tangent planes as a point traverses along the curve from one end to the other. The path traced by the isometric map consists of several important features such as sign change points and locations as well as values of local extremas, that uniquely identify the inherent geometric characteristics of each OFFC. Moreover, these features are invariant under changes of location, rotation and scaling of the fingerprint. We have applied our procedure on the NIST4 database consisting of 4,000 fingerprint images without any training. Classification into four major fingerprint classes (arch, left-loop, right-loop and whorl) with no reject options yields an accuracy of 94.4.% 1