An Efficient Iris Coding Based on Gauss-Laguerre Wavelets

2011

Recognition refers to the problem of establishing a subject's identity from a set of already known identities. Iris recognition system identifies a person from the database of iris images. Iris patterns form distinguishing characteristics for an individual. The potency of iris recognition lies in its textual information. Iris based security systems capture iris patterns of individuals and match the patterns against the record in available databases. In this paper, wavelet decomposition is applied on iris patterns. The magnitude of coefficients aid in the generation of unique code for recognition. The recognition rate of 100% is achieved.

Iris recognition is a critical area of research in the field of security system and personal identification. In this paper, a novel, efficient technique for iris recognition is presented. The aim is to develop a lifting wavelet based algorithm. This method reduces the noise to the maximum extent possible, and extracts important information from the image. In this paper recognition is being done biorthogonal wavelet based method which is then compared with other wavelet family. The method was tested on the CASIA dataset of iris image. For matching the iris template Hamming Distance technique is used and checks the accuracy of the system. The present algorithm provides the 94.4% recognition rate and accuracy approx. 99 % on the CASIA iris data base version 1. https://sites.google.com/site/ijcsis/

2009

There have been numerous implementations of security system using biometric, especially for identification and verification cases. An example of pattern used in biometric is the iris pattern in human eye. The iris pattern is considered unique for each person. The use of iris pattern poses problems in encoding the human iris. In this paper, an efficient iris recognition method is proposed. In the proposed method the iris segmentation is based on the observation that the pupil has lower intensity than the iris, and the iris has lower intensity than the sclera. By detecting the boundary between the pupil and the iris and the boundary between the iris and the sclera, the iris area can be separated from pupil and sclera by means of the Hough transformation. A step is taken to reduce the effect of eyelashes and specular reflection of pupil. Then a four levels Daubechies wavelet transform is applied to the extracted iris image. The modified Hamming distance is employed to measure the simil...

International Journal of Engineering Sciences & Research Technology, 2014

Iris recognition is a potential tool in secure personal identification and authentication system which has the properties such as uniqueness, non-invasiveness and stability of human iris patterns. The method proposed in this paper, differed from the existing work in the segmentation phase where segmentation is done using a different morphological method. Also, comparison is done betw method, for their accuracy in detecting the pupil and iris region. The localized iris image is then normalized and Mallat’s fast wavelet transform is used for feature extraction. The obtained iris templa Hamming distance. The algorithm proposed in this method provides accurate features as well as simple and fast iris analysis. Digitized grayscale images from Chinese Academy of Sciences were used for determining the performance of the proposed system

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004

In this paper, we present a new method for iris identification particularly convenient for visible light images. It relies on the use of packets of wavelets [8] for the production of an iris code. Experiments, conducted on a database of 700 iris images, acquired with visible light illumination, show an improvement of 2% of FAR and of around 11.5% of FRR with the proposed method relatively to the classical wavelet method [1]. The contribution of colour information is also studied with such method.

2013

One of the most reliable biometric is the iris, due to its stability, uniqueness and noninvasive nature. One of the difficult problems in feature based iris recognition is the speed of matching, which is significantly influenced by time required for feature extraction process, size of the template database stored, ...etc. In this paper, a new approach of iris image compression and feature extraction based on discrete wavelet transformation(DWT) is applied. The obtained features dimensionality were further reduced by using principle component analysis(PCA), which drastically reduces the size of the iris database images. In the matching stage, a supervised classifier is introduced, namely, k-nearest neighbor(k-NN). The classification attained was 99.5%. This result shows that the proposed technique is robust and effective compared with other recent works. KeywordsImage preprocessing, DWT, PCA, k-NN, Iris recognition, classification

A new iris recognition system based on Wavelet Packet Analysis and Morlet wavelet is described. Morlet wavelet calculations are easy compared to Gabor wavelets. Moreover Gabor wavelet based iris recognition system is patented which blocks its further development. The most unique phenotypic feature visible in a person's face is the detailed texture of each eye's iris. The visible texture of a person's iris is encoded into a compact sequence of 2-D Morlet wavelet coefficients, which generate an "iris code" of 2025-bits. Two different iris codes are compared using exclusively OR comparisons. In this paper, we propose a novel multi-resolution approach based on Wavelet Packet Transform (WPT) for iris texture analysis and recognition. The development of this approach is motivated by the observation that dominant frequencies of iris texture are located in the low and middle frequency channels. With an adaptive threshold, WPT sub images coefficients are quantized into 1, 0 or -1 as iris signature. This signature presents the local information of different irises. The signature of the new iris pattern is compared against the stored pattern after computing the signature of new iris pattern and identification is performed.

2007 International Conference on Emerging Technologies, 2007

Biometric systems are getting more attention in the present era. Iris recognition is one of the most secure and authentic among the other biometrics and this field demands more authentic, reliable and fast algorithms to implement these biometric systems in real time. In this paper, an efficient localization technique is presented to identify pupil and iris boundaries using histogram of the iris image. Two small portions of iris have been used for polar transformation to reduce computational time and to increase the efficiency of the system. Wavelet transform is used for feature vector generation. Rotation of iris is compensated without shifts in the iris code. System is tested on Multimedia University (MMU) Iris Database and results show that proposed system has encouraging performance.

This paper presents a biometric recognition system based on the iris of a human eye using wavelet transform. The proposed system includes three modules: image preprocessing, feature extraction, and recognition modules. The feature extraction module adopts the gradient directions (i.e., angles) on wavelet transform as the discriminating texture features. The identification system encodes the features to generate the iris codes using two simple and efficient coding techniques: binary Gray encoding and delta modulation. The experimental results show that the recognition rates up to 95.27%, 95.62%, 96.21%, and 99.05%, respectively, using different coding methods can be achieved.

2009

Iris recognition is known as an inherently reliable technique for human identification. Feature extraction is a crucial step for iris recognition and important task. The extracted features are used for matching. Different wavelet transforms have been used by different researchers for feature extraction in iris recognition. Also many researchers extract features using different coefficient such has horizontal, vertical, diagonal or combination of them. In this work we have used SCOE-Iris v.1 database consisting of 2750 images, acquired in the signal processing laboratory at Sinhgad College of Engineering, Pune, India. In this work different basis wavelet are used for feature extraction and their performance is evaluated. The optimal wavelet transform is determined. Experimental results show that this algorithm are efficient and gives acceptable accuracy