The iris is the colored portion of the eye surrounding the pupil. Images captured in the visible spectrum make it difficult for the rich texture of brown irides to be discerned; therefore, iris recognition systems typically capture an image in the Near Infrared (NIR) spectrum. The region surrounding the iris, the ocular region, is also captured by the sensor during the imaging process.The focus of this thesis is on developing methods for predicting soft biometric attributes of an individual based on the iris and ocular components of the eye. In addition to attribute prediction, the effect of covariates on attribute prediction are also studied. Attributes considered in this work include gender, race and eye color. For the gender and race attributes, both the iris and surrounding ocular region are analyzed to determine which region provides the greatest gender cues. A regional analysis reveals that the iris-excluded ocular region provides a greater gender prediction accuracy than the iris-only region. This finding is of great significance as, typically, the iris-excluded ocular region is discarded by the iris recognition system. This research reinforces the need to retain the iris-excluded ocular region for additional processing. For race, it is shown that the iris-only region provides better prediction accuracy. In order to study the stability of the gender and race features, the impact of image blur on attribute prediction was also examined. It is observed that as the level of image blur increases, the race prediction accuracy decays at a much faster rate than that of gender. For eye color, the textual cues presented on the iris stroma are exploited to generate a discriminatory feature vector that is capable of distinguishing between two categories of eye color. The impact of image resolution on attribute prediction was also determined. A convolutional neural network architecture is presented that is capable of attribute prediction using images as small as 5x6, a mere 30 pixels. Experimental results suggest the possibility of deducing soft biometric attributes from low resolution images, thereby underscoring the feasibility of extracting these attributes from poor quality images. Finally, the thesis explores the possibility of harnessing the feature vector used to predict one attribute (e.g., gender) in order to predict a different attribute (e.g. race). The ensuing experiments convey the viability of cross attribute prediction in the context of NIR ocular images.In summary, this thesis provides insight into attribute prediction from NIR ocular images by conducting an extensive set of experiments.
Read
