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In this study, a computational algorithm has been developed to automatically detect human face and irises from color images captured by real-time camera. Haar cascade-based algorithm has been applied for simple and fast face detection. The face image is then converted into grayscale image. Three types of image processing techniques have been tested...
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In numerous science applications, face detection and iris extraction have been recognized as crucial stages by getting more consideration among researchers as it has an important job. This paper presents an automatic detection method of the iris and its center detection by applying the Haar Cascade Classifier and the Circular Hough Transform algori...
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... As such, with further testing of different cell lines and cell types using the experimental setup with manual confirmation using traditional methods (i.e., a haemocytometer), a database can be established wherein the expected power loss corresponds to the current growth rate of any particular cell line. The use of data and single processing techniques, modified from those used in references [32,33] will also be able to improve the performance of the sensor significantly. proliferation (i.e., growth rate) of the culture can be measured. ...
An efficient and low cost optical method for directly measuring the concentration of homogenous biological solutes is proposed and demonstrated. The proposed system operates by Fresnel reflection, with a flat-cleaved single-mode fiber serving as the sensor probe. A laser provides a 12.9 dBm sensor signal at 1,550 nm, while a computer-controlled optical power meter measures the power of the signal returned by the probe. Three different mesenchymal stem cell (MSC) lines were obtained, sub-cultured and trypsinized daily over 9 days. Counts were measured using a haemocytometer and the conditioned media (CM) was collected daily and stored at -80 °C. MSCs release excretory biomolecules proportional to their growth rate into the CM, which changes the refractive index of the latter. The sensor is capable of detecting changes in the number of stem cells via correlation to the change in the refractive index of the CM, with the measured power loss decreasing approximately 0.4 dB in the CM sample per average 1,000 cells in the MSC subculture. The proposed system is highly cost-effective, simple to deploy, operate, and maintain, is non-destructive, and allows reliable real-time measurement of various stem cell proliferation parameters.
... Variability from one image to another is caused by location and orientation of face relative to the image border, lighting, background and facial expressions. Therefore, iris location can be a reference point to locate the face accurately [4]. ...
... Second, the images have to undergo white spot deletion [4]. This is to reduce the reflection of lighting on the iris. ...
... The resolution of the final images will be 10x10 pixels. (4) shows the projection of a set of Eigen Vectors generated from Wavelet Coefficient (V, Column Vector) onto Wavelet Coefficients (W) to generate a set of projected vectors (P). ...
An automated algorithm to localize irises for Middle East individuals had been developed in this research. Histogram equalization, Logabout, Difference of Gaussian (DoG), wavelet transformation, Principle Component Analysis (PCA) and Artificial Neural Network are popular techniques used for image processing, feature extraction and classification. A fusion of these techniques had been introduced to compensate the effects of illumination and head orientation for iris detection. The algorithm was tested with Middle East face database through experiments. In this paper, iris candidates are extracted from the valley of the detected face region after being pre-processed. All the detected iris candidates will go through wavelet transform. The wavelet coefficients are then reduced and extracted by PCA. Finally, Softmax Backpropagation Neural Network (SBNN) works as the iris classifier. The impact of the pre-processing techniques on the performance of the proposed algorithm was studied. The proposed algorithm had achieved a success rate of 90.5% with 0% false positives being reported. Keywords-iris detection; principle component analysis; neural network; wavelet transform
... For internal dataset, it consists of 10 images per 10 classes where each class were taking at different times, varying the lighting, facial expressions and facial details. Face region is cropped based on iris positions [9]. As for Yale dataset, it is made up of 11 images per 15 classes (165 total images). ...
An illumination normalization technique for face recognition by the fusion of Self Quotient Image (SQI) and LogAbout has been proposed. The proposed method is evaluated by using internal face database and Yale face database. The proposed method had shown promising result from the Equal Error Rate (EER) point of view. Our method reported nearly 10% of improvements on average compared to SQI and LogAbout.
Iris segmentation is the principal task of Iris based biometric identification systems. Always the low contrast between pupil and iris always affects on accuracy of detecting boundary between them. In order to increasing the accuracy of boundary detecting, we propose a new technique using a difference function and a factor matrix. We also enhance the technique to detect the pupil and iris as ellipse instead of circle. Experiments show that the proposed technique can segment the iris region and pupil region precisely. Based on our result, 99.34% of eyes have been segmented accurately in 1.24s averagely.
The Iris based identification systems have been noticed exceedingly, presently. In this process, the iris should be segmented from captured eye image. The low contrast between pupil and iris, usually, will harden the segmenting process and will decrease the accuracy of detecting boundary between them. In order to segment the iris more precise, we propose a new technique using a difference function and a factor matrix. We also modify the segmentation operator to detect the pupil and iris as ellipse instead of circle. Experiments show that the proposed technique can segment the iris region and pupil region precisely. Based on our result, 99.34% of eyes have been segmented accurately in 1.24s averagely.
