Iris image compression using wavelets transform coding

Abstract

Iris recognition system for identity authentication and verification is one of the most precise and accepted biometrics in the world. Portable iris system mostly used in law enforcement applications, has been increasing more rapidly. The portable device, however, requires a narrow-bandwidth communication channel to transmit iris code or iris image. Though a full resolution of iris image is preferred for accurate recognition of individual, to minimize time in a narrowbandwidth channel for emergency identification, image compression should be used to minimize the size of image. This paper has investigated the effects of compression particularly for iris image based on wavelet transformed image, using Spatialorientation tree wavelet (STW), Embedded Zero tree Wavelet (EZW) and Set Partitioning in hierarchical trees (SPIHT), to identify the most suitable image compression. In this paper, Haar wavelet transform is utilized for image compression and image decomposition, by varying the decomposition level. The results have been examined in terms of Peak signal to noise ratio (PSNR), Mean square Error (MSE), Bit per Pixel Ratio (BPP)and Compression ratio (CR). It has been evidently found that wavelet transform is more effective in the image compression, as recognition performance is minimally affected and the use of Haar transform is ideally suited. CASIA, MMU iris database have been used for this purpose. Keywords-iris recognition; image compression; mean square error; peak signal to noise ratio(PSNR); wavelet decompositionI.

Quality and texture analysis of biometric images compressed with second-generation wavelet transforms and SPIHT-Z encoder

“…Finally, we define our proposed global image texture quality (ITQ) as a following textural features: (10) We note that Haralick texture features are common texture descriptors in image analysis. To calculate these features, the image gray-levels are reduced using a quantization process that make results depending heavily on the quantization step.…”

“…In this standard, they define the lossy techniques used in data compression such as JPEG, WSQ and JPEG2000 applied in these biometric images. In some previous works, authors evaluate and verify the compression of the biometric images and their influence on biometric recognition systems [5][6][7][8][9][10][11][12]. The important goal of these studies is to study the impact of some compression algorithms like LZW, JPEG variants, WSQ, JPEG2000 and Wavelet packets (PWT) on the biometric images quality or the biometric recognition process using fingerprints, faces, iris and ear images.…”

Lossy Compression of Biometric Images Implemented Using Floating Point DSP Processor

“…An ideal color image with 640 x 480 pixels resolution contains nearly a million elements meanwhile a 512x512 gray image may have 262144 components available for storage. It is also a time consuming process to transfer all the records from the biometric sensor to the running internet servers that classifies the process [7].…”

An efficient iris code storing and searching technique for Iris Recognition using non-homogeneous K-d tree

“…J.Daughman [8] proposed many new approaches to iris recognition with suggestions on improving the iris localization methods. Paul.A, et al, [17] examined the image compression technique on Iris images. Kevin W. Bowyer et al, [13] have carried out extensive survey on history and current trends in iris recognition techniques.…”