Bakhtiar Affendi Rosdi

Finger Vein Recognition Using Local Line Binary Pattern

In this paper, a personal verification method using finger vein is presented. Finger vein can be considered more secured compared to other hands based biometric traits such as fingerprint and palm print because the features are inside the human body. In the proposed method, a new texture descriptor called local line binary pattern (LLBP) is utilized as feature extraction technique. The neighbourhood shape in LLBP is a straight line, unlike in local binary pattern (LBP) which is a square shape. Experimental results show that the proposed method using LLBP has better performance than the previous methods using LBP and local derivative pattern (LDP).

Fusion of Band Limited Phase Only Correlation and Width Centroid Contour Distance for finger based biometrics

A new finger vein recognition algorithm based on Band Limited Phase Only Correlation.Finger width and Centroid Contour Distance for finger geometry recognition.The fusion of vein and geometry for a finger based bimodal biometrics system.A new infrared finger image database is made publicly available on the web. In this paper, a new approach of multimodal finger biometrics based on the fusion of finger vein and finger geometry recognition is presented. In the proposed method, Band Limited Phase Only Correlation (BLPOC) is utilized to measure the similarity of finger vein images. Unlike previous methods, BLPOC is resilient to noise, occlusions and rescaling factors; thus can enhance the performance of finger vein recognition. As for finger geometry recognition, a new type of geometrical features called Width-Centroid Contour Distance (WCCD) is proposed. This WCCD combines the finger width with Centroid Contour Distance (CCD). As compared with the single type of feature, the fusion of W and CCD can improve the accuracy of finger geometry recognition. Finally, we integrate the finger vein and finger geometry recognitions by a score-level fusion method based on the weighted SUM rule. Experimental evaluation using our own database which was collected from 123 volunteers resulted in an efficient recognition performance where the equal error rate (EER) was 1.78% with a total processing time of 24.22ms.

Finger Vein Recognition Algorithm Using Phase Only Correlation

In this paper, we propose an algorithm for finger vein recognition with less complexity in the image preprocessing phase, where finger vein pattern extraction is not included at all. In the proposed algorithm, we implement phase-only correlation (POC) function at the matching stage with a very simple preprocessing technique. Experimental evaluation of the proposed algorithm using a set of finger vein images captured from a low cost device have resulting an efficient recognition performance where the equal error rate (EER) was 0.9803% with a total processing time of 0.6362s.

Finger-vein identification using pattern map and principal component analysis

In this paper, we propose a new approach for finger-vein recognition which uses pattern map based on pixel-pattern-based texture feature (PPBTF) and principal component analysis (PCA). Instead of obtaining finger-vein features from multi-filtered images, we obtain the features from pattern map images. The pattern map images are generated from pattern templates which are the eigenveins obtained from PCA process. Every finger-vein image is transformed into pattern map images where edges and lines are used for characterizing the vein pattern information. PCA is then adopted to further reduce the dimension of the features and nearest neighbour is used for classification. Experiment results show that the proposed algorithm has higher identification rate compared to the existing method with only 40 features. This shows that pattern map is able to represent finger-vein pattern effectively.

Intelligent Biometric Group Hand Tracking (IBGHT) database for visual hand tracking research and development

With the increase of innovations in vision-based hand gesture interaction system, new techniques and algorithms are being developed by researchers. However, less attention has been paid on the scope of dismantling hand tracking problems. There is also limited publicly available database developed as benchmark data to standardize the research on hand tracking area. For this purpose, we develop a versatile hand gesture tracking database. This database consists of 60 video sequences containing a total of 15,554 RGB color images. The tracking sequences are captured in different situations ranging from an easy indoor scene to extremely high challenging outdoor scenes. Complete with annotated ground truth data, this database is made available on the web for the sake of assisting other researchers in the related fields to test and evaluate their algorithms based on standard benchmark data.

Prediction of Antimicrobial Peptides Based on Sequence Alignment and Support Vector Machine-Pairwise Algorithm Utilizing LZ-Complexity

This study concerns an attempt to establish a new method for predicting antimicrobial peptides (AMPs) which are important to the immune system. Recently, researchers are interested in designing alternative drugs based on AMPs because they have found that a large number of bacterial strains have become resistant to available antibiotics. However, researchers have encountered obstacles in the AMPs designing process as experiments to extract AMPs from protein sequences are costly and require a long set-up time. Therefore, a computational tool for AMPs prediction is needed to resolve this problem. In this study, an integrated algorithm is newly introduced to predict AMPs by integrating sequence alignment and support vector machine- (SVM-) LZ complexity pairwise algorithm. It was observed that, when all sequences in the training set are used, the sensitivity of the proposed algorithm is 95.28% in jackknife test and 87.59% in independent test, while the sensitivity obtained for jackknife test and independent test is 88.74% and 78.70%, respectively, when only the sequences that has less than 70% similarity are used. Applying the proposed algorithm may allow researchers to effectively predict AMPs from unknown protein peptide sequences with higher sensitivity.

FPGA-based hardware accelerator for the prediction of protein secondary class via fuzzy K-nearest neighbors with Lempel–Ziv complexity based distance measure

Abstract Correct prediction of protein secondary structural classes is vital for the prediction of tertiary structures and understanding of their function. Most of the prediction algorithms require lengthy computation time. Nearest neighbor – complexity distance measure (NN-CDM) algorithm was one of the significant prediction algorithms using Lempel–Ziv (LZ) complexity-based distance measure, but it is slow and ineffective in handling uncertainties. To solve the problems, we propose fuzzy NN-CDM (FKNN-CDM) algorithm that incorporates the confidence level of prediction results and enhance the prediction process by designing hardware architecture that implements the proposed algorithm in an FPGA board. Highest average prediction accuracies for Z277 and 25PDB datasets using proposed algorithm are 84.12% and 47.81% respectively, with 15 times faster computation using an Altera DE2-115 FPGA board.

Frog Identification System Based on Local Means K-Nearest Neighbors with Fuzzy Distance Weighting

Frog identification based on the vocalization becomes important for biological research and environmental monitoring. As a result, different types of feature extractions and classifiers have been employed. Yet, the k-nearest neighbor (kNN) is one of the popular classifiers and has been applied in various applications. This paper proposes an improvement of kNN in order to evaluate the accuracy of frog sound identification. The recorded sounds of 12 frog species obtained in Malaysia forest have been segmented using short time energy and short time average zero crossing rate while the features are extracted by mel frequency cepstrum coefficient. Finally, a proposed classifier based on local means kNN and fuzzy distance weighting have been employed to identify the frog species. Comparison of the system performances based on kNN, local means kNN and the proposed classifier i.e. fuzzy kNN with manual segmentation and automatic segmentation is evaluated. The results show the proposed classifier outperforms the baseline classifier with accuracy of 94.67 % and 98.33 % for manual and automatic segmentation, respectively.

Synchronous design of 8259 Programmable Interrupt Controller

This paper presents the design of a synchronous 8259 Programmable Interrupt Controller (PIC) that is functionally compatible with the existing asynchronous design of 8259 PIC. The main objective is to reduce the design review efforts along the process technology migration. It also serves as the solutions for the disadvantages and potential hazards inherited in the asynchronous 8259 PIC such as timing loops, race conditions, undetectable signal pulse width and glitches. It is a clock gated synchronous design with only flip flops as the memory elements in it. This synchronous design is implemented using a standard-cell based 32 nm CMOS process. Pre-layout simulation results demonstrate an equivalent interrupt handling mechanism with approximate increase of 0.3% in total gate count and 12.2% in area correspondingly compared to the existing design. Although there is increment of 4.7uW in total dynamic power consumption, but the range of ‘uW’ is acceptable. It can be explained by the higher switching activity of the gated clock signal in the synchronous 8259 PIC compared to the handshaking signal in the asynchronous counterpart.

Multi-Clock Cycle Paths and Clock Scheduling for Reducing the Area of Pipelined Circuits

A new algorithm is proposed to reduce the number of intermediate registers of a pipelined circuit using a combination of multi-clock cycle paths and clock scheduling. The algorithm analyzes the pipelined circuit and determines the intermediate registers that can be removed. An efficient subsidiary algorithm is presented that computes the minimum feasible clock period of a circuit containing multi-clock cycle paths. Experiments with a pipelined adder and multiplier verify that the proposed algorithm can reduce the number of intermediate registers without degrading performance, even when delay variations exist.