A New Approach of Iris Detection and Recognition

<span>Human pupil eye detection is a significant stage in iris segmentation which is representing one of the most important steps in iris recognition. In this paper, we present a new method of highly accurate pupil detection. This method is consisting of many steps to detect the boundary of the pupil. First, the read eye image (R, G, B), then determine the work area which is consist of many steps to detect the boundary of the pupil. The determination of the work area contains many circles which are larger than pupil region. The work area is necessary to determine pupil region and neighborhood regions afterward the difference in color and intensity between pupil region and surrounding area is utilized, where the pupil region has color and intensity less than surrounding area. After the process of detecting pupil region many steps on the resulting image is applied in order to concentrate the pupil region and delete the others regions by using many methods such as dilation, erosion, canny filter, circle hough transforms to detect pupil region as well as apply optimization to choose the best circle that represents the pupil area. The proposed method is applied for images from palacky university, it achieves to 100 % accurac</span>

Section: Introductionmentioning

confidence: 99%

Pupil Detection Based on Color Difference and Circular Hough Transfor

Daway

Kareem

Hashim

2018

“…For the feature extraction process, different methodologies were used to retrieve features in previous studies and these included Sample Entropy [9], Autoregressive (AR) Model [10], Discrete Wavelet Transform (DWT) [11], and Fast Fourier Transformation (FFT) [12]. Classification of these extracted features was done using various classifiers by the researchers such as Support Vector Machine (SVM) [13,14], Neural Network [15], and k-nearest neighbor (KNN) [16].…”

Section: Introductionmentioning

confidence: 99%

“…This lead to less computational cost and thus increased the efficiency of the algorithms used in our technique [19]. Existing methods, working with frequency bands, extracted all the five types of frequency bands, namely delta (0.5-4 Hz), theta (4-7 Hz), alpha (7-13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma [20]. According to the state-of-the-art methods, the emotional and cognitive activities of the brain can be well signified using the alpha, beta, and theta frequency bands [8], [21].…”

Section: Introductionmentioning

confidence: 99%

Recognition of emotional states using EEG signals based on time-frequency analysis and SVM classifier

George

Shaikat

Hossain

et al. 2019

Self Cite

The recognition of emotions is a vast significance and a high developing field of research in the recent years. The applications of emotion recognition have left an exceptional mark in various fields including education and research. Traditional approaches used facial expressions or voice intonation to detect emotions, however, facial gestures and spoken language can lead to biased and ambiguous results. This is why, researchers have started to use electroencephalogram (EEG) technique which is well defined method for emotion recognition. Some approaches used standard and pre-defined methods of the signal processing area and some worked with either fewer channels or fewer subjects to record EEG signals for their research. This paper proposed an emotion detection method based on time-frequency domain statistical features. Box-and-whisker plot is used to select the optimal features, which are later feed to SVM classifier for training and testing the DEAP dataset, where 32 participants with different gender and age groups are considered. The experimental results show that the proposed method exhibits 92.36% accuracy for our tested dataset. In addition, the proposed method outperforms than the state-of-art methods by exhibiting higher accuracy.

“…Biometric system such as Rhythmprint authentication, which combines an advantage of the traditional password-based authentication and a multi-touch innovation based on a touchpad on a computer or a screen of a smartphone [5] provides high security and can mitigate shoulder-surfing. Face recognition system [6], iris detection and recognition [7], ear recognition method [8], electrocardiogram (ECG) signal waveform [9], etc presents recent innovations in biometrics that can be applied to reinforce the biometric security of ATM system. However, most of this technology are in their infancy and have not been applied for the security of ATM machines.…”

Section: Introductionmentioning

confidence: 99%

“…Our approach is different from other approach found in the literature because it does not mitigate shoulder-surfing but it eradicates the possibility of shoulder-surfing, eavesdropping, and man-in-the-middle attack. The inspiration behind this research relies on the hypothesis that iris identification technology is widely accepted and is gaining ground as the best authentication system as it has high reliability, unique and distinctive (even identical twins do not share the same pattern of iris) [7]. The verification process is swift, the iris texture remains stable (it does not change), it can be captured from a distance without harm to the eye.…”

Section: Introductionmentioning

confidence: 99%

Fingereye: improvising security and optimizing ATM transaction time based on iris-scan authentication

Omolara

Jantan

Arshad

et al. 2019

The tumultuous increase in ATM attacks using eavesdropping, shoulder-surfing, has risen great concerns. Attackers often target the authentication stage where a customer may be entering his login information on the ATM and thus use direct observation techniques by looking over the customer's shoulder to steal his passwords. Existing authentication mechanism employs the traditional password-based authentication system which fails to curb these attacks. This paper addresses this problem using the FingerEye. The FingerEye is a robust system integrated with iris-scan authentication. A customer’s profile is created at registration where the pattern in his iris is analyzed and converted into binary codes. The binary codes are then stored in the bank database and are required for verification prior to any transaction. We leverage on the iris because every user has unique eyes which do not change until death and even a blind person with iris can be authenticated too. We implemented and tested the proposed system using CIMB bank, Malaysia as case study. The FingerEye is integrated with the current infrastructure employed by the bank and as such, no extra cost was incurred. Our result demonstrates that ATM attacks become impractical. Moreover, transactions were executed faster from 6.5 seconds to 1.4 seconds.