Gender and Ethnicity Classification of Iris Images using Deep Class-Encoder
This work addresses the problem of improving biometric recognition efficiency by classifying soft traits like gender and ethnicity from iris images, which is incremental as it builds on existing soft biometric approaches.
The paper tackled gender and ethnicity classification from iris images using a novel supervised autoencoder called Deep Class-Encoder, which maps features to labels for discriminative representation, and demonstrated effectiveness compared to existing methods on two datasets.
Soft biometric modalities have shown their utility in different applications including reducing the search space significantly. This leads to improved recognition performance, reduced computation time, and faster processing of test samples. Some common soft biometric modalities are ethnicity, gender, age, hair color, iris color, presence of facial hair or moles, and markers. This research focuses on performing ethnicity and gender classification on iris images. We present a novel supervised autoencoder based approach, Deep Class-Encoder, which uses class labels to learn discriminative representation for the given sample by mapping the learned feature vector to its label. The proposed model is evaluated on two datasets each for ethnicity and gender classification. The results obtained using the proposed Deep Class-Encoder demonstrate its effectiveness in comparison to existing approaches and state-of-the-art methods.