High-order information for robust iris recognition under less controlled conditions

Iris recognition has achieved great progress in cooperative environments in the past decades. However, in less controlled conditions it is still an open and challenging problem because of severe noisy factors induced by non-cooperative subjects. For handling this challenging problem, we propose a method called ordinal measure of outer product tensor (O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PT) which leverages the high-order information of image features. O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PT consists of two components. First we compute outer product tensors of raw features (e.g. SIFT) which are vectorized and locally aggregated, characterizing the second-order statistics of raw features. And then we compute the ordinal measure of the aggregated outer product tensors to model the order relation of iris texture, which makes the representation more compact and robust to noise and illumination changes. Furthermore, we combine two modalities to improve the matching performance, namely, O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PT for iris image matching and Fisher Vector (FV), which also exploits the high-order information, for eye image matching. We have achieved competitive matching performance on the challenging UBIRIS.v2 and CASIA-Iris-Thousand databases.