Secure Digital Camera Based Fingerprint Recognition System

Touch-less fingerprint recognition has been receiving attention recently and it imposes a competency for a further advancing of this technology as it frees from the problems in term of hygienic, maintenance and latent fingerprints

A Coevolutioanary Neural Network for Detecting Chemical Gas Sensor Drift

Sensor drift is a phenomenon which indicates unexpected variations in the sensory signal responses beneath the same working conditions. In this paper, a competitive co-evolutionary (ComCoE) Multilayer Perceptron artificial neural network (MLPN) is applied to detect chemical gas sensor drift. The efficiency of the ComCoE MLPN in detecting chemical gas sensor drift is evaluated as well as compared with the performance of other classification methods from the literature. The proposed ComCoE MLPN has shown promising preliminary results in this applicatio

Camera-based signature verification system through Discrete Radon Transform (DRT) and Principle Component Analysis (PCA)

This paper presents a low cost camera-based signature verification system by using discrete Radon transform (DRT) and principle component analysis (PCA). The system is tested on independent database, and reported with false acceptance rate (FAR) of 7% for random forgery; and 27% for skilled forgery

A double-elimination-tournament-based competitive co-evolutionary artificial neural network classifier

This paper presents a competitive co-evolutionary (ComCoE) that engages a double elimination tournament (DET) to evolve artificial neural networks (ANNs) for undertaking data classification problems. The proposed model performs a global search by a ComCoE approach to find near optimal solutions. During the global search process, two populations of different ANNs compete and fitness evaluation of each ANN is made in a subjective manner based on their participations throughout a DET which promotes competitive interactions among individual ANNs. The adaptation and fitness evaluation processes drive the global search for a more competent ANN classifier. A winning ANN is identified from the global search. Then, the Scaled Conjugate Backpropagation algorithm, which is a local search, is performed to further train the winning ANN to obtain a precise solution. The performance of the proposed classification model is evaluated rigorously; its performance is compared with the baseline ANNs of the proposed model as well as other classifiers. The results indicate that the proposed model could construct an ANN which could produce high classification accuracy rates with a compact network structure

Intra-specific competitive co-evolutionary artificial neural network for data classification

This paper presents an alternative approach of competitive co-evolutionary (ComCoE) artificial neural network (ANN) developed for data classification. The motivation of this work is to employ an interactive game-based fitness evaluation method within a CoE framework to develop a compact and accurate ANN model. The proposed model uses only one population of radial basis function artificial neural networks (RBFANNs) in the CoE framework to find out an optimised RBFANN. In the ComCoE process, the RBFANNs compete in an intra-specific competition environment, which is driven by a game-based fitness evaluation method. The fitness evaluation for each RBFANN is made by computing the interaction among the selected RBFANNs in a population quantitatively throughout a number of encounters under a Single Elimination Tournament topology. Two indicators, i.e. the classification accuracy and hidden nodes number of each RBFANN, are referred to compute the fitness value. The proposed model performs a global search for finding potential near optimal solution. Then, a local search (Backpropagation algorithm) is executed to reach at a precise solution. The proposed classification model is evaluated using 14 public data sets from the UCI machine-learning repository. A performance comparison between the proposed model and other state-of-art classifiers is also conducted. The empirical results show that the proposed model, which constructs a compact network structure, could perform with high classification accuracy rates

A secure digital camera based fingerprint verification system

Contemporary fingerprint system uses solid flat sensor which requires contact of the finger on a platen surface. This often results in several problems such as image deformation, durability weakening in the sensor, latent fingerprint issues which can lead to forgery and hygienic problems. On the other hand, biometric characteristics cannot be changed; therefore, the loss of privacy is permanent if they are ever compromised. Coupled with template protection mechanism, a touch-less fingerprint verification system is further provoked. In this issue, a secure end-to-end touch-less fingerprint verification system is presented. The fingerprint image captured with a digital camera is first pre-processed via the proposed pre-processing algorithm to reduce the problems appear in the image. Then, Multiple Random Projections-Support Vector Machine (MRP-SVM) is proposed to secure fingerprint template while improving system performance

Touch-less Fingerprint Recognition System

Touch-less fingerprint recognition is regarded as a viable alternative to contact-based fingerprint recognition technology. It provides a near ideal solution to the problems in terms of hygienic, maintenance and latent fingerprints. In this paper, we present a touch-less fingerprint recognition system by using a digital camera. Specifically, we address the constraints of the fingerprint images that were acquired with digital camera, such as the low contrast between the ridges and the valleys in fingerprint images, defocus and motion blurriness. The system comprises of preprocessing, feature extraction and matching stages. The proposed preprocessing stage shows the promising results in terms of segmentation, enhancement and core point detection. Feature extraction is done by Gabor filter and the favorable verification results are attained with the Support Vector Machine

Reissuable Biometrics through Image-Based Handwritten Signature Verification

The privacy invasion of the biometric technology is getting public concerns due to the fact that biometric characteristics are immutable. In other words, their compromise is permanent. Reissuable biometrics was devised to make the reissuable or replaceable of biometric templates possible once they are found compromised. Biometric Strengthening is a form of reissuable biometrics. It strengthens the biometric templates by transforming the original template values to form a new set of values through the Gaussian distribution. The performance of Biometric Strengthening is evaluated in three possible intrusion scenarios. Probabilistic neural network (PNN) is employed as classifier. The compatibility of Biometric Strengthening and PNN shows the potential of using them in real world application. The experiments are tested on own image-based handwritten signature data set due to the lack of benchmark database

Digital camera based fingerprint recognition

Touch-less fingerprint recognition deserves increasing attention as it lets off the problems of deformation, maintenance, latent fingerprint problems and so on that still exist in the touch-based fingerprint technology. However, problems such as the low ridges-valleys contrast in the fingerprint images, defocus and motion blurriness raise when developing a digital camera based fingerprint recognition system. The system comprises of preprocessing, feature extraction and matching stages. The proposed preprocessing stage presents the promising results in terms of segmentation, enhancement and core point detection. Feature extraction is done by Gabor filter followed by Principle Component Analysis (PCA) and the favorable verification results are attained with Cosine Angle

Offline Signature Verification through Probabilistic Neural Network

In this paper, we show the positive potential of verifying the offline handwritten signatures through discrete Radon transform (DRT), principle component analysis (PCA) and probabilistic neural network (PNN). Satisfactory results are obtained with 1.51%, 3.23%, and 13.07% equal error rate (EER) for random, casual, and skilled forgeries respectively on our independent database