[PERSON] [17] computed curvature on the extracted hand contour to detect the fingertips and valleys. The curvature then served as the primary geometric feature, while finger length, width and the angle of finger valley were determined to serve as secondary geometric features. This technique, however, required pegs to stabilize the hand before the image acquisition in order to achieve a system invariant to the geometric transformation of the hand relative to the camera coordinate system. [PERSON] and [PERSON] [18] proposed a peg-free hand shape analysis and recognition. In the technique they proposed, ellipse fitting was modelled to the finger regions to derive the major and minor axes of each finger, which were later used to estimate the finger rotation and translation. The technique was limited in the case of rigid transformation. [PERSON] et al. [19] applied a principle component analysis (PCA) to the finger width and length of five fingers and used it for geometric-based features; however. this technique also assumed a rigid transformation. [PERSON] et al. [20] presented another peg-free hand geometric identification system. This technique determined the tip of the middle finger and normalized the rotation by aligning the straight line joining the hand centroid and middle fingertip horizontally. This technique reportedly performed well in the case of rigid transformation. [PERSON] et al. [21] used the ratios of structural lengths (e.g., the ratio of index/middle finger, ratio of palm length/ palm width) to derive a system invariant to similarity transformation and immune to the scale parameters. Other related work using geometric-based features included [22]. Vein pattern feature methods exploit the uniqueness of the superficial vasculature inside the human body. Areas such as the hands, fingers and wrists provide a source of promising biometric data that remains relatively unaffected by aging. To effectively visualize these vein patterns, infrared imaging technology is required. As deoxyhemoglobin in the vein absorbs more infrared, the intensity of venous regions in the image appears as darker pixel vales compared with the surrounding tissues. [PERSON] et al. [23] used near infrared (NIR) for vein pattern imaging. The disadvantage of this NIR imaging system was that defections on skin surface were visible in the image, requiring their removal with image processing. To extract features, morphological thinning was applied to the vein pattern and Housdorff distance was used as a similarity measure [24]. [PERSON] et al. [25] applied NIR for the real-time vein pattern imaging of the subcutaneous dorsal hand. This system did not require a fixed setup to place the hand during data acquisition as they had developed a self-locating algorithm. This was used in conjunction with a two-axis pan-tilt mechanism to locate the palm dorsum automatically, irrespective of the initial positioning of the palm dorsum by the person under testing. [PERSON] et al. [26] proposed another dorsal hand subcutaneous vein pattern NIR imaging system that was reportedly immune to the undesirable effects of visible light by surrounding the system with a rectangular box with a specially designed opening for the hand. In addition, a handle was added inside the imaging box for the user to grasp during the image acquisition process. This was to make the system invariant to rigid transformation, i.e., the hand location and orientation parameters. Related works of vein pattern-based personal verification and/or identification systems using the hand and/or finger images are reviewed in [27; 28; 29; 30]. This paper proposes a hybrid-feature dorsal hand vein and dorsal geometry modality for human recognition. The salient aspects and/or contributions of this paper are enumerated as follows. (1) This paper presents the development of a real-time, low-cost and easy-to-implement vein pattern personal verification system for human recognition.