Iris Recognition Using Wavelet Features

2009 International Conference on Digital Image Processing, 2009

We propose a novel approach for improved iris recognition system with reduced false acceptance rate (FAR) and false rejection rate (FRR). The technique developed here uses all the frequency resolution planes of Discrete Wavelet Transform (DWT). These frequency planes provide abundant texture information present in an iris at different resolutions. The accuracy is improved up to 98.98%. With proposed method FAR and FRR is reduced up to 0.0071% and 1.0439% respectively.

Procedia Engineering, 2012

In view of the limitation of poor direction selectivity about 2-D wavelet transform and the problem of redundancy on contourlet transform, an iris texture feature extraction method based on wavelet-based contourlet transform (WBCT)for obtaining high quality features is proposed in the paper. Firstly, the preprocessed iris image is decomposed by WBCT, then calculating its energy, mean, standard deviation and Hu invariant moments of each subband of different scales and different directions, and taking them as the eigenvalues of iris image, finally, it tests on four iris image databases by using Euclidean distance. Experimental results show that the algorithm is simple and effective, and obtain better recognition performance.

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...

Lecture Notes in Computer Science, 2009

In different methods of Biometrics, recognition by iris images in recent years has been taken into consideration by researchers as one of the common methods of identification like passwords, credit cards or keys. Iris recognition a new biometric technology has great advantages such as variability, stability and security. In this paper we propose a new feature extraction method for iris recognition based on contourlet transform. Contourlet transform captures the intrinsic geometrical structures of iris image. It decomposes the iris image into a set of directional sub-bands with texture details captured in different orientations at various scales so for reducing the feature vector dimensions we use the method for extract only significant bit and information from normalized iris images. In this method we ignore fragile bits. At last the feature vector is approximated by non linear approximation coefficient. Experimental results show that the proposed method reduces processing time and increase the classification accuracy and outperforms the wavelet based method.

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.

2019

Personal identification has become the most important factor in authentication processes in recent years. The various biometric identifiers such as face, iris, fingerprint, hand, voice, signature recognition are used for human identification. Among all these biometric identifiers, Iris recognition is the most reliable and accurate biometric identifier as iris cannot be forged. This paper project involves the acquisition of the image of an eye followed by the segmentation and localization of the image to obtain the image of iris. The Haar wavelet is used for feature extraction of an image. Hamming distance is measured between the image in the database and the detected image. Iris recognition is then performed by matching the iris pair with the minimum Hamming distance.

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

International Journal of Engineering and Artificial Intelligence , 2020

The recent advancement in technology over the last decade has feature a major focus of research and development on biometric methods for the identification of individual. A biometric system offers automatic identification of an individual based on a unique feature or characteristic possessed by the individual, this feature can be physiological or biological. Features extraction is the process of extracting information from the iris image and thus a crucial stage in the iris recognitions process. In this study, we utilized an enhanced feature extraction technique based on continuous wavelet Transformation (CWT) for the extraction of iris features and reduced the runtime needed for iris templates classification. Experimental evaluation demonstrates the efficient performance of the proposed technique. Compared with Gabor filter, Fourier transform and other wavelet transforms, the proposed approach considerably reduces the computation time and improves the accuracy. The performance of continuous wavelet transform (CWT) for the feature extraction shows to be more stable and consistent even with a very high level of noisy data as compared to discrete wavelet transform (DWT). The proposed method has 0.8% FAR, 1.4% FRR and 97.8% performance recognition accuracy.

2014

Iris is the annular region of the eye bounded by the pupil and the sclera(white of the eye) on either side. The iris has many interlacing features such as stripes, freckles, coronas, radial furrow, crypts, zigzag collarette, rings etc collectively referred to as texture of the iris. This texture is well known to provide a signature that is unique to each subject. All these features are extracted using different algorithms i.e features extraction is the process of extracting information from the iris image. Iris feature extraction is the crucial stage of the whole iris recognition process for personal identification. This is a key process where the two dimensional image is converted to a set of mathematical parameters. The significant features of the iris must be encoded so that comparisons between templates can be made. In this study the feature of the iris is extracted using Fast Wavelet Transform (FWT). The algorithm is fast and has a low complexity rate. The system encodes the fe...

2017

Biometric system is used for Identification as well as verification purpose. This paper presents the comparative of feature extraction of Iris. Iris Biometric system has image Acquisition, Image preprocessing, Feature extraction and matching. The features are extracted from the pre-processed images of iris images by using Gaussian filter as well as by using Gabor wavelet. The quality measures of the images are calculated. The Gabor wavelet improves the performance of the system.