Extracting rotation and translation invariant features is a difficult task for palmprint recognition. Traditional methods have difficulty in dealing with palmprint images degraded by those variations. Studies have shown that neurons at higher levels exhibit an increasing degree of invariance to above mentioned image variations. Moreover, primary visual cortex(V1) is believed to give stronger responses to light bars of certain directions. Based on these observations, a biologically inspired transform feature extractor, namely BIT, for palmprint recognition is proposed in this paper. BIT involves two stages, which mimics visual information processing in V1. In the first stage, we build an orientation edge detector to highlight the edges response in each direction. The orientation edge detector is primarily composed of a phase congruency based edge detector and a bipolar filter. After that, a local spatial frequency detector produces a response, converting rotation factors of orientation edges into a horizontal shifted map. In the second stage, the orientation edge detector and local spatial frequency detector are applied again, which converts shifted map into an invariant pixel in feature map. Extensive experimental results not only show that our method is robust to image variations including rotation and translation, but also illustrate the effectiveness and discriminability of the extracted invariant palmprint features in recognition problems.