22-05-2012, 05:24 PM
EYE DETECTION USING VARIENTS OF HOUGH TRANSFORM
[attachment=22783]
ABSTRACT
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.
Overview:
One of the basic problems in the design of an object recognition system is the selection of a set of statistical features to be extracted from the original image for the purpose of classification. Without classification it is very difficult to recognize any object. Objects are recognized from many different vantage points (from the front, side, or back), in many different places, and in different sizes. Objects can even be recognized when they are partially obstructed from view.
Biometric Technology:
A biometric system provides automatic recognition of an individual based on some sort of unique feature or characteristic possessed by the individual. Biometric systems have been developed based on fingerprints, facial features, voice, hand geometry, handwriting, the retina, and the one presented in this project, the eye. Biometric systems work by first capturing a sample of the feature, such as recording a digital sound signal for voice recognition, or taking a digital colour image for eye detection. The sample is then transformed using some sort of mathematical function into a biometric template. The biometric template will provide a normalized, efficient and highly discriminating representation of the feature, which can then be objectively compared with other templates in order to determine identity. Most biometric systems allow two modes of operation. A training mode or enrolment mode for adding templates to a database, and an identification mode, where a template is created for an individual and then a match is searched for in the database of pre-enrolled templates. A good biometric is characterized by use of a feature that is; highly unique so that the chance of any two people having the same characteristic will be minimal, stable so that the feature does not change over time, and be easily captured in order to provide convenience to the user, and prevent misrepresentation of the feature.
[attachment=22783]
ABSTRACT
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.
Overview:
One of the basic problems in the design of an object recognition system is the selection of a set of statistical features to be extracted from the original image for the purpose of classification. Without classification it is very difficult to recognize any object. Objects are recognized from many different vantage points (from the front, side, or back), in many different places, and in different sizes. Objects can even be recognized when they are partially obstructed from view.
Biometric Technology:
A biometric system provides automatic recognition of an individual based on some sort of unique feature or characteristic possessed by the individual. Biometric systems have been developed based on fingerprints, facial features, voice, hand geometry, handwriting, the retina, and the one presented in this project, the eye. Biometric systems work by first capturing a sample of the feature, such as recording a digital sound signal for voice recognition, or taking a digital colour image for eye detection. The sample is then transformed using some sort of mathematical function into a biometric template. The biometric template will provide a normalized, efficient and highly discriminating representation of the feature, which can then be objectively compared with other templates in order to determine identity. Most biometric systems allow two modes of operation. A training mode or enrolment mode for adding templates to a database, and an identification mode, where a template is created for an individual and then a match is searched for in the database of pre-enrolled templates. A good biometric is characterized by use of a feature that is; highly unique so that the chance of any two people having the same characteristic will be minimal, stable so that the feature does not change over time, and be easily captured in order to provide convenience to the user, and prevent misrepresentation of the feature.