Iris recognition system by using support vector machines

Ali, Hasimah; Salami, Momoh Jimoh Eyiomika; Wahyudi, Wahyudi

doi:10.1109/iccce.2008.4580657

Cited by 15 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Features are usually defined and selected according to a problem-oriented strategy, that is, ad hoc in light of the information considered relevant for the task at hand. In forensics, a plethora of features have been defined for the automated solutions to different problems, such as face detection, retrieval and recognition in video and images [58][59][60], individual people tracking over video sequences [61,62], recognition of different biometric parameters (ear, gait, and iris) in images or videos [63,64], speaker identification in audio signals, suspicious word detection, and handwriting recognition in text document.…”

Section: State Of the Artmentioning

confidence: 99%

Novel Methods for Forensic Multimedia Data Analysis: Part I

Perner¹

2020

Digital Forensic Science

View full text Add to dashboard Cite

The increased usage of digital media in daily life has resulted in the demand for novel multimedia data analysis techniques that can help to use these data for forensic purposes. Processing of such data for police investigation and as evidence in a court of law, such that data interpretation is reliable, trustworthy, and efficient in terms of human time and other resources required, will help greatly to speed up investigation and make investigation more effective. If such data are to be used as evidence in a court of law, techniques that can confirm origin and integrity are necessary. In this chapter, we are proposing a new concept for new multimedia processing techniques for varied multimedia sources. We describe the background and motivation for our work. The overall system architecture is explained. We present the data to be used. After a review of the state of the art of related work of the multimedia data we consider in this work, we describe the method and techniques we are developing that go beyond the state of the art. The work will be continued in a Chapter Part II of this topic.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Novel Methods for Forensic Multimedia Data Analysis: Part I

Perner¹

2020

Digital Forensic Science

View full text Add to dashboard Cite

show abstract

“…Most commonly used kernel functions are polynomial, sigmoid and Gaussian radial basis function. The SVM in general makes four possible decision in iris recognition; the authorized person is accepted, the authorized person is rejected, the unauthorized person (impostor) is accepted and the unauthorized person (impostor) is rejected [18] .…”

Section: A Support Vector Machinesmentioning

confidence: 99%

Iris Pattern Recognition using Support Vector Machines and Artificial Neural Networks

Khan¹,

Kumar²,

Khan³

2014

International Journal of Innovative Research in Electrical, Ele

View full text Add to dashboard Cite

An authentic personal identification infrastructure is required to control the access in order to secure areas or materials. Biometric technology is based on physiological or behavioral characteristics of a human body. Iris recognition system consists of image acquisition, localization, normalization, features extraction and encoding, and classification. Iris images are downloaded from CASIA Iris V1.0 database for study. To separate the iris region from the eye image, Hough transform is used. Circular Hough transform is used to localize circular iris and pupil region while parabolic Hough transform is used to enhance the occluding eyelids and eyelashes. Daugman"s rubber sheet model is used to normalize the extracted iris region into a rectangular block with constant polar dimensions. After normalization, 2D Gabor filter is employed to extract the important features from iris. Iris provides texture information which is unique, universal and contains high randomness. Feature extraction is performed by convolving the normalized iris region with 2D Gabor filter which gives the phase information. The phase data represented by a data set is utilized as input for classifiers. The classifiers used in this study are Artificial Neural Networks (ANN) and Support Vector Machines (SVM). This study shows that Support Vector Machines gives higher recognition rate than Artificial Neural Networks.

show abstract