Rotation, scaling and translation (RST) invariant features extraction with bionic vision mechanism is proposed to improve their recognition accuracy and robustness. Inspired by the biological visual perception, the cortical cells are able to balance the selectivity and invariance of the image with multiple transformations. Therefore, the proposed method is devided into two stages. In the first stage, inspired by the horizontal and vertical directions response of biological vision, a novel filtertofilter orientation edge detector is built that combines Gabor filters and bipolar filters. The Gabor filters are used as the bottom filter to smooth images, and the edge detector is constructed by the edge of the horizontal and vertical bipolar filter, to enhance the feature extraction robustness and the edge detection accuracy. On this basis, response intensity of cortical visual cortex cells are simulated and the spatial frequency of image is measured according to the different edge direction and distance. Furthermore, spatial frequency interval detector is designed by orientationinterval image mapping in θ-I coordination, which transforms rotation and scaling of original image into a horizontal or vertical shift. In the second stage, the orientation and interval detection are performed once again on the output of first stage, which converts the horizontal and vertical shift into an invariant pixel in orientationinterval map to make the imaoge RST invariance. Experimental results illustrate the effectiveness of invariant features and recognition ability. Meanwhile, the recognition accuracy and complexity are compared with other invariant feature extraction methods, which shows the proposed method is superior on robustness to rotation, scaling, translation and noise image.