Elliptical higher-order-spectra periocular code

The periocular region has recently emerged as a standalone biometric trait, promising attractive trade-off between the iris alone and the entire face, especially for cases where neither the iris nor a full facial image can be acquired. This advantage provides another dimension for implementing a robust biometric system, performed in non-ideal conditions. Global features (LBP, HOG) and local features (SIFT) have been introduced; however, the performance of these features can deteriorate for images captured in unconstrained and less-cooperative conditions. A particular set of Higher Order Spectral (HOS) features have been proved to be invariant to translation, scale, rotation, brightness level shift and contrast change. These properties are desirable in the periocular recognition problem to deal with the non-ideal imaging conditions. This paper investigates the HOS features in different configurations for the periocular recognition problem under non-ideal conditions. Especially, we introduce a new sampling approach for the periocular region based on an elliptical coordinate. This non-linear sampling approach is then combined with the robustness of the HOS features for encoding the periocular region. In addition, we also propose a new technique for combining left and right periocular. The proposed feature-level fusion approach bases on state-of-the-art bilinear pooling technique to allow efficient interaction between the features of both perioculars. We show the validity of the proposed approach in encoding discriminant features, outperforming or comparing favorably with the state-of-the-art features on the two popular datasets: FRGC and JAFFE

Periocular recognition under expression variation using Higher Order Spectral features

Periocular recognition promises attractive trade-off between iris recognition and face recognition because it provides a longer range of imaging than iris and could achieve a higher recognition performance than face. This benefit is critical to the success of a biometric system under unconstrained and less cooperative operating conditions. A number of feature encoding techniques have been proposed for periocular recognition including global features (SIFT, HOG) and local features (LBP). However, these features perform poorly for images captured in non-ideal conditions with the presence of facial expressions. In this paper, we investigate periocular recognition dealing with the deformation caused by facial expressions. In addition, we also investigate a novel feature encoding technique, called Higher Order Spectral features, on periocular images. We show that our proposed approach toward features for the periocular recognition under facial expressions using Higher Order Spectra is effective in encoding discriminant features. The proposed approach is validated on the JAFFE dataset