Bee Yan Hiew

Integrating Palmprint and Fingerprint for Identity Verification

In this paper, we propose a multimodal biometrics system that combines fingerprint and palmprint features to overcome several limitations of unimodal biometrics—such as the inability to tolerate noise, distorted data and etc.—and thus able to improve the performance of biometrics for personal verification. The quality of fingerprint and palmprint images are first enhanced using a series of pre-processing techniques. Following, a bank of 2D Gabor filters is used to independently extract fingerprint and palmprint features, which are then concatenated into a single feature vector. We conclude that the proposed methodology has better performance and is more reliable compared to unimodal approaches using solely fingerprint or palmprint biometrics. This is supported by our experiments which are able to achieve equal error rate (EER) as low as 0.91% using the combined biometrics features.

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.

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.

Automatic Digital Camera Based Fingerprint Image Preprocessing

Touch-less fingerprint recognition has been receiving attention recently as it frees from the problems in terms of hygienic, maintenance and latent fingerprints. However, the conventional techniques that used to preprocess the optical or capacitance sensor acquired fingerprint image, for segmentation, enhancement and core point detection, are inadequate to serve the purpose. The problems of the touch-less fingerprint recognition consist of low contrast between the ridges and the valleys in fingerprint images, defocus and motion blur. In this paper, we opt for digital camera as the device to capture the fingerprint image in RGB format and we outline the procedures to segment, enhance and detect the core point of the fingerprint image

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 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.

Development of a Co-evolutionary Radial Basis Function Neural Classifier by a k-Random Opponents Topology

The interest of the research in this paper is to introduce a novel competitive co-evolutionary (ComCoE) radial basis function artificial neural network (RBFANN) for data classification. The motivation is to derive a compact and accurate RBFANN by implementing an interactive “game-based” fitness evaluation within a ComCoE framework. In the CoE process, all individual RBFANNs interact with each other in an intra-specific competition. The fitness of each RBFANN is evaluated by measuring its interaction/encounter with k number of other randomly picked RBFANNs in the same population through a quantitative yet subjective manner under a k-random opponents topology. To calculate the fitness value, both the hidden nodes number and classification accuracy of each RBFANN are taken into consideration. To obtain a potential near optimal solution, the proposed model performs a global search through ComCoE approach and then performs a local search that is initiated by a scaled conjugate backpropagation algorithm to fine-tune the solution. Results from a benchmark study show high effectiveness of the co-evolved model with a k-random opponents topology in constructing an accurate yet compact network structure.