In this research, we propose an innovative touch-less palm print recognition system. This project is
motivated by the publicís demand for non-invasive and hygienic biometric technology. For various reasons,
users are concerned about touching the biometric scanners. Therefore, we propose to use a low-resolution
web camera to capture the userís hand at a distance for recognition. The users do not need to touch any
device for their palm print to be extracted for analysis. A novel hand tracking and palm print region of
interest (ROI) extraction technique are used to track and capture the userís palm in real time video streams.
The discriminative palm print features are extracted based on a new way that applies local binary pattern
(LBP) texture descriptor on the palm print directional gradient responses. Experiments show promising
result by using the proposed method. Performance can be further improved when a modified probabilistic
neural network (PNN) is used for feature matching.
ō Hand Tracking and ROI Extraction
∑ Skin-Colour Thresholding
∑ Valley Detection
∑ ROI Location
ō Image Pre-processing