Biometric systems perform recognition of individuals on the basis of their physical and/or behavioral traits. Some commonly used traits are fingerprint, face, iris, retina, palm print, voice pattern, signature, gait, etc. Fusion of Biometric traits improves the system performance and accurarcy. Fingerprint are detailed, unique, difficult to alter and durable over a lifetime. Features are ... [Show full abstract] extracted from fingerprint using Thepade's sorted n-ary block truncation coding. Thepade's sorted ternary block truncation coding (TSTBTC) using level 2 is used to reduce the feature vector size of biometric traits. Left hand fingerprint and Right hand fingerprint are taken together to improve accuracy in terms of genuine acceptance ratio(GAR) in Multimodal Biometrics Identification.The test bed of 40 pairs of Left hand finger and right hand finger (4 left hand fingerprint and 4 right hand fingerprint per person ) are used as test bed for experimentation. Different color spaces are used on images for improvement in genuine acceptance ratio(GAR). Matching Score level Fusion is considered for Experimentation. RGB, Ycgcbr, Yuv, Kluv, Yiq, Ycbcr color spaces are considered among that RGB color space for multimodal fusion give high GAR than other color spaces.