Circle-based Eye Center Localization (CECL)

We propose an improved eye center localization method based on the Hough transform, called Circle-based Eye Center Localization (CECL) that is simple, robust, and achieves accuracy on a par with typically more complex state-of-the-art methods. The CECL method relies on color and shape cues that distinguish the iris from other facial structures. The accuracy of the CECL method is demonstrated through a comparison with 15 state-of-the-art eye center localization methods against five error thresholds, as reported in the literature. The CECL method achieved an accuracy of 80.8% to 99.4% and ranked first for 2 of the 5 thresholds. It is concluded that the CECL method offers an attractive alternative to existing methods for automatic eye center localization.Comment: Published and presented at The 14th IAPR International Conference on Machine Vision Applications, 2015. http://www.mva-org.jp/mva2015

Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images

Iris centre localization in low-resolution visible images is a challenging problem in computer vision community due to noise, shadows, occlusions, pose variations, eye blinks, etc. This paper proposes an efficient method for determining iris centre in low-resolution images in the visible spectrum. Even low-cost consumer-grade webcams can be used for gaze tracking without any additional hardware. A two-stage algorithm is proposed for iris centre localization. The proposed method uses geometrical characteristics of the eye. In the first stage, a fast convolution based approach is used for obtaining the coarse location of iris centre (IC). The IC location is further refined in the second stage using boundary tracing and ellipse fitting. The algorithm has been evaluated in public databases like BioID, Gi4E and is found to outperform the state of the art methods.Comment: 12 pages, 10 figures, IET Computer Vision, 201

A. Eye Detection Using Varients of Hough Transform B. Off-Line Signature Verification

PART (A): EYE DETECTION USING VARIANTS OF HOUGH TRANSFORM: Broadly eye detection is the process of tracking the location of human eye in a face image. Previous approaches use complex techniques like neural network, Radial Basis Function networks, Multi-Layer Perceptrons etc. In the developed project human eye is modeled as a circle (iris; the black circular region of eye) enclosed inside an ellipse (eye-lashes). Due to the sudden intensity variations in the iris with respect the inner region of eye-lashes the probability of false acceptance is very less. Since the image taken is a face image the probability of false acceptance further reduces. Hough transform is used for circle (iris) and ellipse (eye-lash) detection. Hough transform was the obvious choice because of its resistance towards the holes in the boundary and noise present in the image. Image smoothing is done to reduce the presence of noise in the image further it makes the image better for further processing like edge detection (Prewitt method). Compared to the aforementioned models the proposed model is simple and efficient. The proposed model can further be improved by including various features like orientation angle of eye-lashes (which is assumed constant in the proposed model), and by making the parameters adaptive. PART (B): OFF-LINE SIGNATURE VERIFICATION: Hand-written signature is widely used for authentication and identification of individual. It has been the target for fraudulence ever since. A novel off-line signature verification algorithm has been developed and tested successfully. Since the hand-written signature can be random, because of presence of various curves and features, techniques like character recognition cannot be applied for signature verification. The proposed algorithm incorporates a soft-computing technique “CLUSTERING” for extraction of feature points from the image of the signature. These feature points or centers are updated using the clustering update equations for required number of times, then these acts as extracted feature points of the signature image. To avoid interpersonal variation 6 to 8 signature images of the same person are taken and feature points are trained. These trained feature points are compared with the test signature images and based on a specific threshold, the signature is declared original or forgery. This approach works well if there is a high variation in the original signature, but for signatures with low variation, it produces incorrect results

Intergrated leachate treatment by sequencing batch reactor (SBR) and micro-zeolite (MZ)

Biological treatment has a lot of potential in leachate treatment with the ability of the biodegradable substrates and this method can reduce the cost of treatment residues with respect to ecological and economical requirements.The aims of this study are to investigate the effect of biological treatment by using sequencing batch reactor (SBR) system in different condition consisting anaerobic (An), anoxic (Ax), and oxic (Ox) with different reaction time. An integration in combining phases consisting An/Ax/Ox is used in order to achieve maximum removal. Then, followed by the performance of combination phases consisting An/Ax/Ox in SBR system with addition of adsorption adsorbent micro-zeolite (MZ) (size range 75-150 μm) at different dosages. The raw leachate and sludge were collected from sanitary landfill from Tanjung Langsat, Pasir Gudang. An condition has better performance in an SBR system at optimum reaction time 11 hr promoting the percentage removal efficiency of chemical oxygen demand (COD), ammonia nitrogen (AN), total nitrogen (TN), total phosphorus (TP) and suspended solid (SS) and turbidity which were 77%, 74.65%, 75.07%, 76.05%, 63.91%, and 62.67% respectively. The result indicated that the combined condition consisting An/Ax/Ox at the optimum time reaction of each condition gives the removal efficiency COD, AN, TN, TP, SS, and turbidity which were 85.78%, 88.65%, 87.07%, 86.9%, 81.92% and 81.15% respectively. The application addition of adsorption adsorbent gives optimum dosage at 5 g/L. The addition of MZ shows good removal efficiency which were more than 90% at overall parameter

An approach towards iris localization for non cooperative images: A study

Iris localization is the most important part of iris recognition which involves the detection of iris boundaries in an image. A very important need of this effective security system is to overcome the rigid constraints necessitated by the practical implementation of such a system. There are a few existing techniques for iris segmentation in which iris detection using Circular Hough Transform is the most reliable and popular and it has been implemented in this project. But there is a shortcoming in this technique. It does not perform well and does not gives high accuracy with images containing noise or occlusions caused by eyelids. Such kind of images constitute non cooperative data for iris recognition. To provide acceptable measures of accuracy, it is critical for an iris recognition system to overcome various noise effects introduced in images captured under different environment such as occlusions due to eyelids. This report discusses an approach towards less constraint iris recognition using occluded images. The Circular Hough Transform is implemented for few images and a novel approach towards iris localization and eyelids detection is studied.