Vessel Extraction on Ocular Fundus Images by Using Gabor Filter Bank

Fundus diagnosis is an important part of the whole body examination that may provide rich clinical information to doctors for diagnostic reference. Manual fundus vessel extraction is helpful to quantitative measurement of diseases but obviously it is a tough work for physicians. This paper presents an automatic method by using Gabor filter bank to extract the artery and vein separately in the ocular fundus images. After preprocessing steps that include gray-scale transform, gray value inversion and contrast enhancement, the Gabor filter bank is applied to the extraction of the artery and vein in the ocular fundus images. Finally these two different width types of vessels are selected by post-processing methods such as labeling, corrosion, binarization, etc. Evaluation results show an accurate rate of 90% in vein and 82% in artery from 20 cases, that indicates the effectiveness of our proposed segmentation method.

Face Recognition Based on Gabor Feature Extraction Followed by FastICA and LDA

Over the past few decades,face recognition has become the most effective biometric technique in recognizing people’s identity,as it is widely used in many areas of our daily lives.However,it is a challenging technique since facial images vary in rotations,expressions,and illuminations.To minimize the impact of these challenges,exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features.Therefore,this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction,Fast Independent Component Analysis(FastICA),and Linear Discriminant Analysis(LDA).In the presented method,first,face images are transformed to grayscale and resized to have a uniform size.After that,facial features are extracted from the aligned face image using Gabor,FastICA,and LDA methods.Finally,the nearest distance classifier is utilized to recognize the identity of the individuals.Here,the performance of six distance classifiers,namely Euclidean,Cosine,Bray-Curtis,Mahalanobis,Correlation,and Manhattan,are investigated.Experimental results revealed that the presented method attains a higher rank-one recognition rate compared to the recent approaches in the literature on four benchmarked face datasets:ORL,GT,FEI,and Yale.Moreover,it showed that the proposed method not only helps in better extracting the features but also in improving the overall efficiency of the facial recognition system.