Iris Image Compression using Wavelets Transform Coding Arnob Paul 1 , Tanvir Zaman Khan 2 , Prajoy Podder 3 , Rafi Ahmed 4 , M. Muktadir Rahman 5 , and Mamdudul Haque Khan 6 1 Department of ECE at Institute of Engineering & Management (IEM), Kolkata, India 2,3,4,5,6 Department of ECE at Khulna University of Engineering & Technology (KUET) Khulna-9203, Bangladesh arnobpaul11@gmail.com 1 , tzkhan19@gmail.com 2 , prajoypodder@gmail.com 3 , salehinrafi@yahoo.com 4 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 narrow- bandwidth 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 Spatial- orientation 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 decomposition I. INTRODUCTION Image compression plays an essential role for effective transmission and storage of images. Televideo conferencing, medical imaging, document processing, remote object sensing etc. are the most significant applications of image compression. [1]. Requirements for storage, management and transfer of digitized image, have grown explosively using digital cameras. These stored image size can be very big and can use a lots of memory of the storage device. For example a 512x512 gray image has more than 50,000 components available for storage; on the other hand an ideal color image that is 640 x 480 pixels has closely a million elements. It is very time consuming job to copy or download these records from the internet servers. Indian government launched “Aadhaar” program in 2010 to collect the biometric categorizing features specifically iris patterns for nearly about 1.2 billion Indian residents [2]. This system storage is too high to manage including database transferring over internet or designing a portable device to carry. This actually leads us to compress iris image based on wavelet transform without loss of iris features. It is also necessary to measure the recognition performance of compressed iris image [3], [4]. In general image occupies the vital portion of bandwidth for communication. Therefore the improvement of efficient image compression technique has turned into quite compulsory [5]. The fundamental aim of image compression is to remove redundancy and omit irrelevancy. Redundancy helps to remove redundancy from the signal source and irrelevancy omits pixel values which are not noticeable by the human eye. This paper can be organized as follows. The next section discussed the related works of image compression in case of iris recognition system. Third section familiarizes the different forms of image compression technique based on wavelet transform. The working procedure of proposed method has been discussed in the fourth section. The experimental results have been focused in the fifth section. Finally, conclusions are given in Section VI. II. RELATED WORKS Compression technique can be applied to image to reduce their storage size and transmission time. There are two kinds of compression such as Lossless and lossy compressions. During the last few years several image compression techniques have been developed in biometric system like iris recognition system. Different types of image compression standards like JPEG, JPEG-2000 and JPEG-XR have been utilized to generate the compact iris data [2], [3], [4]. Daugman proposed JPEG compression technique with region of interest isolation. He adopted his method in one database [2]. R.W Ives investigated the result of image compression and performance of iris recognition scheme along with JPEG-2000 compression technique [12]. Funk et al. investigated and discussed the impact of JPEG, JPEG-2000 (ISO/IEC 15444), fractal, PRVQ image compression on cross over accuracy of biometric system [13]. But the JPEG technique computes the DCT of 8x8 blocks taken from the original eye image. But sometimes JPEG is not suitable for high compression rates. Another limitation is that the blocking artifact that can occur at high compression ratio. This paper has proposed a suitable iris image compression technique using different wavelets that can be applied in iris 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN) 978-1-4799-5991-4/15/$31.00 ©2015 IEEE 544