Shahrel Azmin Suandi

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).

Recent survey on crowd density estimation and counting for visual surveillance

Automated crowd density estimation and counting are popular and important topic in crowd analysis. The last decades witnessed different of many significant publications in this field and it has been and still a challenging problem for automatic visual surveillance over many years. This paper presents a survey on crowd density estimation and counting methods employed for visual surveillance in the perspective of computer vision research. This survey covers two main approaches which are direct approach (i.e., object based target detection) and indirect approach (e.g. pixel-based, texture-based, and corner points based analysis). This review categorizes and delineates several crowd density estimation and counting methods that have been applied for the examination of crowd scenes.

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.

Hand gesture tracking system using Adaptive Kalman Filter

This paper introduces a hand tracking system in unconstrained environment. The system consists of two main stages which are initialization and tracking. In initialization, hand region is first detected by combining motion and skin color pixels. A region of interest (ROI) is then created around the detected hand region. In tracking stage, skin and motion pixels are scanned around top, left and right corners of the ROI to detect the moving hand in consecutive video frames. These pixels are used to actually measure the ROI position and fed into measurement update of Adaptive Kalman Filter (AKF) operation. The process noise covariance and measurement noise covariance of AKF are adjusted adaptively by applying weighting factor based on acceleration threshold value. The experimental result shows the proposed method has the robust ability to track the moving hand under real life scenarios at speed 45fps with average 97.83% tracking rate.

Hybrid Human Skin Detection Using Neural Network and K-Means Clustering Technique

Proposed skin detection method. The proposed method is a hybrid method that combines the neural networks and k-means.Modelling skin with different color-texture descriptors was investigated. High accuracy of F1=87.82% can be achieved based on images from the ECU database.Study showed that YIQ color space gives the highest separability in skin detection. Human skin detection is an essential step in most human detection applications, such as face detection. The performance of any skin detection system depends on assessment of two components: feature extraction and detection method. Skin color is a robust cue used for human skin detection. However, the performance of color-based detection methods is constrained by the overlapping color spaces of skin and non-skin pixels. To increase the accuracy of skin detection, texture features can be exploited as additional cues. In this paper, we propose a hybrid skin detection method based on YIQ color space and the statistical features of skin. A Multilayer Perceptron artificial neural network, which is a universal classifier, is combined with the k-means clustering method to accurately detect skin. The experimental results show that the proposed method can achieve high accuracy with an F1-measure of 87.82% based on images from the ECU database.

Optimizing principal component analysis performance for face recognition using genetic algorithm

Principal Component Analysis (PCA) turns out to be one of the most successful techniques in face recognition systems as a statistical method for dimensionality reduction. Even so, it is yet not optimal from the perspective of classification because the underlying distribution among different face classes in the image space is unpredicted and not known in advance. Besides, in practical applications, a question always raised on how much data should be included in the training. In this paper, a technique that associates genetic algorithm (GA) to PCA is proposed to maintain the property of PCA while enhancing the classification performance. It reconsiders the available training data and tries to find the best underlying distribution for classification. ORL, and Yale A databases have been used in the experiments to analyze and evaluate the performance of the proposed method compared to original PCA. The experiment results reveal that the proposed method outperforms PCA in terms of accuracy and classification time.

Vehicle Classification Using Visual Background Extractor and Multi-class Support Vector Machines

This paper describes a method to classify vehicle type using computer vision technology. In this study, Visual Background Extractor (ViBe) was used to extract the vehicles from the captured videos. The features of the detected vehicles were extracted using Histogram of Oriented Gradient (HOG). Multi-class Support Vector Machine (SVM) was used to recognise four classes of images: motorcycle, car, lorry and background (without vehicles). The results show that the proposed classifier was able to achieve an average accuracy of 92.3 %.

Object Shape Recognition in Image for Machine Vision Application

—Vision is the most advanced of our senses, so it is not surprising that images contribute important role in human perception. This is analogous to machine vision such as shape recognition application which is important field nowadays. This paper proposed shape recognition method where circle, square and triangle object in the image will be recognizable by the algorithm. This proposed method utilizes intensity value from the input image then thresholded by Otsus method to obtain the binary image. Median filtering is applied to eliminate noise and Sobel operator is used to find the edges. Thinning method is used to remove unwanted edge pixels where these pixels may be counted in the parameter estimation algorithm, hence increase the false detection. The shapes are decided by compactness of the region. The experimental results show that this method archives 85% accuracy when implemented in selected database.

Automated segmentation of iris images acquired in an unconstrained environment using HOG-SVM and GrowCut

Abstract Iris recognition systems have demonstrated considerable improvement in recognizing people through their iris patterns. Recent iris recognition systems have focused on images acquired in unconstrained environments. Unconstrained imaging environments allow the capture of iris images at a distance, in motion and under visible wavelength illumination which lead to more noise factors such as off-focus, gaze deviation, and obstruction by eyelids, eyeglasses, hair, lighting and specular reflections. Segmenting irises taken in an unconstrained environment remains a challenging task for iris recognition. In this paper, a new iris segmentation method is developed and tested on UBIRIS.v2 and MICHE iris databases that reflect the challenges in recognition by unconstrained images. This method accurately localizes the iris by a model designed on the basis of the Histograms of Oriented Gradients (HOG) descriptor and Support Vector Machine (SVM), namely HOG-SVM. Based on this localization, iris texture is automatically extracted by means of a cellular automata which evolved via the GrowCut technique. Pre- and post-processing operations are also introduced to ensure higher segmentation accuracy. Extensive experimental results illustrate the effectiveness of the proposed method on unconstrained iris images.