Gender and ethnicity classification of Iris images using deep class-encoder
Gender and ethnicity classification of Iris images using deep class-encoder
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DOI：
10.1109/btas.2017.8272755
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发表时间：
2017-10
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通讯作者：
Maneet Singh;Shruti Nagpal;Mayank Vatsa;Richa Singh;A. Noore;A. Majumdar
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作者：
Maneet Singh;Shruti Nagpal;Mayank Vatsa;Richa Singh;A. Noore;A. Majumdar
软生物特征模态已经在不同的应用中显示出它们的效用，包括显着减少搜索空间。这导致改进的识别性能、减少的计算时间和测试样本的更快处理。一些常见的软生物特征模态是种族、性别、年龄、头发颜色、虹膜颜色、面部毛发或痣的存在以及标记。本研究针对虹膜影像进行种族与性别分类。我们提出了一种新的基于监督的自动编码器的方法，深度类编码器，它使用类标签通过将学习到的特征向量映射到其标签来学习给定样本的判别表示。该模型在两个数据集上进行评估，每个数据集用于种族和性别分类。与现有方法和最先进的方法相比，使用所提出的深度类编码器获得的结果证明了其有效性。
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 auto-encoder 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.