This paper presents a design of an algorithm for hand vascular pattern extraction algorithm, which is specifically designed for person verification applications. The algorithm is based on a filter bank approach since it applies two different filters to the input hand images; one for effective extraction of normal state blood vessels (normal mode filter) and the other for effective extraction of thin vessels and vessels in a contracted state (enhancement mode filter). We combine output of both filters to obtain the final hand vascular patterns. The filter bank approach has been proven to prevent possible loss of connectivity of vascular patterns in severe variations of weather temperature. In addition, the proposed NMF and EMF are designed to have filter coefficients in the form of power of two, which can be realized using fixed-point operators, and reduces hardware complexity dramatically. In order to evaluate the design, a prototype system has been implemented utilizing a Field Programmable Gate Array (FPGA) device. The prototype system shows approximately five-times better verification error rate than the conventional single filter algorithm. And the person verification speed including feature extraction time is measured to be 133[ms/person].