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.
@cite_19 also did not specify a subject-disjoint experimental protocol. Their proposed method used a variant of an auto-encoder that includes the class label alongside the reconstruction layer. The experiments were performed on the ND-Iris-0405 dataset as well as a multi-ethnicity iris dataset composed of three separate datasets. Each class (Asian, Indian, Caucasian) was represented by a distinct dataset. They achieved a 94.33
@cite_19
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Ethnicity is an important demographic attribute of human beings, and automatic face-based classification of ethnicity has promising applications in various fields. In this paper, we explore the ethnicity discriminability of both 2D and 3D face features, and propose an MM-LBP (Multi-scale Multi-ratio LBP) method, which is a multimodal method for ethnicity classification. LBP (Local Binary Pattern) histograms are extracted from multi-scale, multi-ratio rectangular regions over both texture and range images, and Adaboost is utilized to construct a strong classifier from a large amount of weak classifiers built by the extracted LBP histograms. Decision level fusion is performed to get the final decision. Experiments performed on FRGC v2.0 database indicate that the fusion of 2D and 3D face features significantly improves the classification accuracy, and the proposed MM-LBP method has consistent higher performance for ethnicity classification than traditional methods. Above 99.5 classification accuracy was obtained on the FRGC v2.0 database.