Fingerprint Compression based on Sparse Representation : A Review

Recognition of people by means of their biometric characteristics very popular among the society. Among this, fingerprint recognition is an important technology for personal identification due to its unique structure. Large volume of fingerprint are collected and stored everyday in a wide range of applications. In this context, the compression of these data may become imperative under certain circumstances due to the large amounts of data involved. This paper compare different compression standards like JPEG,JPEG-2000,WSQ,KSVD etc. A new compression standards based on sparse representation also introduced. The experiments demonstrate that this is efficient compared with several competing compression techniques especially at high compression ratios. Keywords— Compression; JPEG;JPEG 2000; K-SVD;WSQ INTRODUCTION Large volumes of fingerprints are collected and keep daily in an exceedingly big selection of applications, including forensics, access management etc., and fingerprint square measure evident from the information of Federal Bureau of Investigation (FBI) . Fingerprint identification is commonly employed in forensic science to support criminal investigations, and in biometric systems such as civilian and commercial identification devices. Since giant volume of information consumes additional amount of memory, the data contained in fingerprints should, therefore, be compressed by extracting solely visible components. Fingerprint pictures exhibit characteristic high energy in bound high frequency bands ensuing from the ridge-valley pattern and alternative structures . The DCT [2]-based encoder is thought as compression of stream of 8X8 little blocks of pictures. This transform is adopted in JPEG[3]. the JPEG compression theme has several benefits like simplicity, catholicity and availability. However, it has a bad performance at low bit-rates mainly due to the underlying block-based DCT scheme. For this reason, as early as 1995, the JPEG-committee began to develop a wavelet-based compression for still images, specifically JPEG 2000[5]. Targeted at fingerprint images, there are special compression algorithms. The most common is Wavelet Scalar Quantization(WSQ)[6]. It became the FBI standard for the compression of 500 dpi fingerprint images. Inspired by the WSQ algorithm, a few wavelet packet based fingerprint compression schemes have been developed. But, these algorithms have a common problem, namely without the ability of learning the fingerprint images can’t be compressed well now. So a novel approach based on sparse representation is given [1]. Here features are extracted and represent them as dictionary atoms. In most Automatic Fingerprint identification System (AFIS), the main feature used to match two fingerprint images are minutiae (ridges endings and bifurcations). Therefore, the difference of the minutiae between preand post-compression is considered in the paper. I. RELATED WORKS In this section, we compare the proposed method with existing fingerprint compression algorithms like JPEG,JPEG-2000,WSQ,K-SVD etc. A. JPEG For the past few years, a joint ISO/CCITT committee known as JPEG (Joint Photographic Experts Group)has been working to establish the first international compression standard for continuous-tone still images. To meet the differing needs of many applications, the JPEG standard includes two basic compression methods i.e. a DCT-based method is specified for “lossy’’compression, and a predictive method for “lossless’’ compression. JPEG has undertaken the ambitious task of developing a generalpurpose compression standard to meet the needs of almost all continuous-tone still-image applications. The JPEG compression scheme has many advantages such as simplicity, universality and availability. However, it has a bad performance at low bit-rates mainly because of the block-based DCT scheme. For this reason, as early as 1995, the JPEG-committee began to develop a new wavelet-based compression standard for still images, namely JPEG 2000. B. JPEG 2000 In 1996, the JPEG committee began to investigate possibilities for a new still image compression standard to serve current and future applications. The desire to provide a broad range of features for numerous applications in a single compressed bit-stream prompted the JPEG committee in 1996 to investigate possibilities for a new compression standard that was named JPEG-2000. In JPEG 2000,DCT of JPEG is replaced with DWT(Discrete Wavelet Transform)[3]. The DWT-based Naja M I et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (1) , 2015, 228-231