Predicting a person's gender based on the iris texture has been explored by several researchers. This paper considers several dimensions of experimental work on this problem, including person-disjoint train and test, and the effect of cosmetics on eyelash occlusion and imperfect segmentation. We also consider the use of multi-layer perceptron and convolutional neural networks as classifiers, comparing the use of data-driven and hand-crafted features. Our results suggest that the gender-from-iris problem is more difficult than has so far been appreciated. Estimating accuracy using a mean of N person-disjoint train and test partitions, and considering the effect of makeup - a combination of experimental conditions not present in any previous work - we find a much weaker ability to predict gender-from-iris texture than has been suggested in previous work.
The extraction of ancillary information from biometric traits is known as . As defined by Dantcheva al @cite_14 , "[s]oft biometric traits are physical, behavioral, or material accessories, which are associated with an individual, and which can be useful for recognizing an individual."
@cite_14
297909767
Recent research has explored the possibility of extracting ancillary information from primary biometric traits viz., face, fingerprints, hand geometry, and iris. This ancillary information includes personal attributes, such as gender, age, ethnicity, hair color, height, weight, and so on. Such attributes are known as soft biometrics and have applications in surveillance and indexing biometric databases. These attributes can be used in a fusion framework to improve the matching accuracy of a primary biometric system (e.g., fusing face with gender information), or can be used to generate qualitative descriptions of an individual (e.g., young Asian female with dark eyes and brown hair). The latter is particularly useful in bridging the semantic gap between human and machine descriptions of the biometric data. In this paper, we provide an overview of soft biometrics and discuss some of the techniques that have been proposed to extract them from the image and the video data. We also introduce a taxonomy for organizing and classifying soft biometric attributes, and enumerate the strengths and limitations of these attributes in the context of an operational biometric system. Finally, we discuss open research problems in this field. This survey is intended for researchers and practitioners in the field of biometrics.