Histogram of Oriented Gradients is one of the most extensively used image descriptors in computer vision. It has successfully been applied to various vision tasks such as localization, classification and recognition. As it mainly captures gradient strengths in an image, it is sensitive to local variations in illumination and contrast. In the result, a normalization of this descriptor turns out to be essential for good performance [3, 4]. Although different normal-ization schemes have been investigated, all of them usually employ L1 or L2-norm. In this paper we show that an in-cautious application of L-like norms to the HOG descrip-tor might produce a hallucination effect. To overcome this issue, we propose a new normalization scheme that effectively minimizes hallucinations. This scheme is built upon a detailed analysis of the gradient distribution resulting in adding an extra bin with a specific value that increases HOG distinctiveness. We validated our approach on person re-identification and action recognition, demonstrating significant boost in the performance.