Recent research has explored the possibility of automatically deducing information such as gender, age and race of an individual from their biometric data. While the face modality has been extensively studied in this regard, the iris modality less so. In this paper, we first review the medical literature to establish a biological basis for extracting gender and race cues from the iris. Then, we demonstrate that it is possible to use simple texture descriptors, like BSIF (Binarized Statistical Image Feature) and LBP (Local Binary Patterns), to extract gender and race attributes from an NIR ocular image used in a typical iris recognition system. The proposed method predicts gender and race from a single eye image with an accuracy of 86 and 90 , respectively. In addition, the following analysis are conducted: (a) the role of different parts of the ocular region on attribute prediction; (b) the influence of gender on race prediction, and vice-versa; (c) the impact of eye color on gender and race prediction; (d) the impact of image blur on gender and race prediction; (e) the generalizability of the method across different datasets; and (f) the consistency of prediction performance across the left and right eyes.
It should also be noted that there are some gender prediction work using the periocular region in the in @cite_30 , @cite_24 , @cite_41 and @cite_37 .
@cite_30 @cite_41 @cite_37 @cite_24
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The periocular region, the region of the face surrounding the eyes, has gained increasing attention in biometrics in recent years. This region of the face is of particular interest when trying to identify a person whose face is partially occluded. We propose the novel idea of applying the information obtained from the periocular region to identify the gender of a person, which is a type of soft biometrie recognition. We gradually narrow the region of interest of the face to explore the feasibility of using smaller, eye-centered regions for building a robust gender classifier around the periocular region alone. Our experimental results show that at least an 85 classification rate is still obtainable using only the periocular region with a database of 936 low resolution images collected from the web. With periocular biometrics gaining attention recently, the goal of this paper is to investigate the effectiveness of local appearance features extracted from the periocular region images for soft biometrie classification. We extract gender and ethnicity information from the periocular region images using grayscale pixel intensities and periocular texture computed by Local Binary Patterns as our features and a SVM classifier. Results are presented on the visible spectrum periocular images obtained from the FRGC face dataset. For 4232 periocular images of 404 subjects, we obtain a baseline gender and ethnicity classification accuracy of 93 and 91 , respectively, using 5-fold cross validation. Furthermore, we show that fusion of the soft biometrie information obtained from our classification approach with the texture based periocular recognition approach results in an overall performance improvement. Gender classification from periocular images is a challenging topic. Previous algorithms have focused primarily on the use of texture features and not much research has been done on applying Convolutional Neural Networks (CNN) to this task. In this work we trained a small convolutional neural network for the left and right eyes, and more importantly, studied the effect of merging those models and compare it against the model obtained by training a CNN over the fused left-right eye images. We show that the network benefits from this model merging approach, and becomes more robust towards occlusion and low resolution degradation, outperforming the results of using a single CNN model for the left and right set of images. Ex-periments done over a database of near-infrared periocular images show that our CNN model exhibits competitive performance compared to other state-of-the-art methods. Previous researchers have explored various approaches for predicting the gender of a person based on the features of the iris texture. This paper is the first to predict gender directly from the same binary iris code that could be used for recognition. We found that the information for gender prediction is distributed across the iris, rather than localized in particular concentric bands. We also found that using selected features representing a subset of the iris region achieves better accuracy than using features representing the whole iris region. We used the measures of mutual information to guide the selection of bits from the iris code to use as features in gender prediction. Using this approach, with a person-disjoint training and testing evaluation, we were able to achieve 89 correct gender prediction using the fusion of the best features of iris code from the left and right eyes.