# Gender and Ethnicity Classification of Iris Images using Deep Class-Encoder

arXiv: https://arxiv.org/abs/1710.02856 · HTML: https://mlsanity.com/paper/1710.02856/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2017.

## Built on by (most cited first, 3 of 3)

- [Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Images](https://mlsanity.com/paper/2103.16760/) (2021)
- [Exploring the Feasibility of Deep Learning Techniques for Accurate Gender Classification from Eye Images](https://mlsanity.com/paper/2508.00135/) (2025)
- [Periocular Soft Biometrics: A Survey and Applications to Multimedia Forensics and Disinformation Detection](https://mlsanity.com/paper/2608.14701/) (2026)

## Abstract (arXiv)

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.