Human identification exploitation biometric traits are more and more in style in recent years. Among the widely used biometric traits, palm-print is a vital one because of its acquisition convenience and comparatively high recognition results. The paper proposes a palm-print recognition system based on quality estimation and feature dimensions. Initially, a quality assessment is applied on the extracted region of interest (ROI) images. Gabor filter is employed to extract the palm-print features having various scales and orientations. The kernel-based dimensionality reduction is applied in the full space that reduces the high-dimensional Gabor features. The experiments are conducted on the PolyU, IIT-Delhi and CASIA palm-print databases. The best recognition performance in terms of an equal error rate (EER) of 0.051% and recognition rate (RR) of 98.34% was achieved on PolyU database. Experimental results prove the effectiveness of the proposed approach.