Hand Vein patterns have been adjudged to be one of the safest biometric modalities due to their strong resilience against the impostor attacks. This paper presents a new approach for biometric authentication using infrared thermal hand vein patterns. In contrast to the existing features for hand vein patterns which are based solely on edge detection, we propose Box and branch point based approaches for multiple feature representations. A robust peg free camera set up is employed for infrared thermal imaging. A region of interest (ROI) is extracted from the vein patterns and is convolved with Gabor filter. The real part of this convolution is only preserved for further processing. Multiple features are extracted from the real parts of the convolved images using the proposed branch point based feature extraction techniques. The multiple features are then integrated at the decision level. AND and OR fusion rules are employed to combine the decisions taken by the individual matcher. Experiments conducted on a database of 100 users result in a False Acceptance Rate (FAR) of 0.1% for the Genuine Acceptance Rate (GAR) of 99% for decision level fusion.