An invariant feature extraction method called GIFT(Gabor scale-Invariant Feature Transform)is proposed

which has multi-characteristic scales.Firstly

by using 2D Gabor filter bank to model the biological cognitive computational model

intuitional and robust keypoints are detected.It is accordant with vision perceptive characteristic.Secondly

multi-characteristic scales of the detected keypoints are selected based on the Gabor kernel function

and then multi-characteristic descriptors with high distinctiveness are obtained.Finally

a feature matching strategy for multi-characteristic scales is designed

which increases the reliability of feature matching.The comparison experimental results on the standard dataset show that the proposed GIFT outperforms SIFT on both feature matching rate and robustness.