Wavelet based palmprint authentication system

A feature level multimodal approach for palmprint identification using directional subband energies

“…gravity center, density, spatial dispersivity and energy used as features for identification (Zhang and Zhang, 2004). By the application of 2-D two band discrete wavelet transform and three band wavelet decomposition on palmprint and using low subband images as identification feature vectors (Zhou et al, 2006;Masood et al, 2008). Further the palmprint features are extracted by local binary patterns (Kah-Ong et al, 2008) in different directions for the process of identification.…”

Palm Print Recognition using Zernike Moments

“…To put the above results in perspective with the state of the art, in [6] authors report an accuracy of 96% using 400 images, in [7] authors report over 99% accuracy when tested on 1440 images, in [8] authors claim the error rate to be below 3%, in [10] an acceptance rate of 97% is reported, in [11] a correct recognition rate of 98% is reported, in [13] an accuracy of 99% is reported.…”

“…The palmprint images are used from Biological Research Center of Hong Kong Polytechnic University (PolyU). In [10] a palm print identification is done using wavelet transforms. The wavelets used for the analysis are Biorthogonal, Symlet and Discrete Meyer.…”

2-D Discrete Wavelet Transform for Hand Palm Texture Biometric Identification and Verification

“…Behavioural character includes signature, voice (which also has a physical component), keystroke pattern, and gait. Nowadays, most of the security systems developed into the society are based on hand image analysis (Masood et al, 2008;Pavesic et al, 2004), especially in the texture of the hand palm since they provide people with higher security in relation to authentication systems (Zhang, 2004 ;Yan & Long, 2008) and offer a good balance of performance characteristics; besides, they are relatively easy to use. Although other biometric techniques like fingerprint or hand and finger geometry let us achieve good results, a security and highly accurate biometric system can be built through the use of palm-print texture features.…”

“…Classifying with the ISODATA algorithm got a 95% of identification accuracy with 180 palm-print of 80 people. Masood et al, 2008, developed a palm-print system using wavelet transforms. 50 people took part in the session (10 samples per person), reaching a 97.12% of accuracy with the combination of different wavelet families.…”