As a vital branch in the study of biometrics-based technology

identification and verification by palm print has been a striking evidence for prior disease diagnosis and personal recognition

given its remarkable advantages like simplicity and stablility

etc. Especially

the extraction process of principle-line feature plays a key role. This paper presents a new approach to extract this novel characteristic. Unlike other traditional methods

its step is inherently simple and convenient using regular scanner. After the pre-process and alignment

we extract four spatial directional template and reach high convergence by adopting Symlet wavelets transformation method

and a series of morphological operations derived from ASF are utilized. Finally we use regression analysis and image fusion to eliminate divergence and disconnectedness in our result region

and successfully extract principle-line from numerous palm-lines. The experimental results with a large collection of different images showed its advantages compared with former work

and also illustrated its strong robustness

and provide effective and accurate statistics to clinical diagnosis

classification and encoding work at a later stage.