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收稿：2017-05-22，
修回：2017-9-21，
纸质出版：2018-01-16
移动端阅览
目的
2
虹膜是位于人眼表面黑色瞳孔和白色巩膜之间的圆环形区域，有着丰富的纹理信息。虹膜纹理具有高度的区分性和稳定性。人种分类是解决虹膜识别在大规模数据库上应用难题的主要方法之一。现有的虹膜图像人种分类方法主要采用手工设计的特征，而且针对亚洲人和非亚洲人的基本人种分类，无法很好地解决亚种族分类问题。为此提出一种基于虹膜纹理深度特征和Fisher向量的人种分类方法。
方法
2
首先用CNN（convolutional neural network）对归一化后的虹膜纹理图像提取深度特征向量，作为底层特征；然后使用高斯混合模型提取Fisher向量作为最终的虹膜特征表达；最后用支持向量机分类得到最终结果。
结果
2
本文方法在亚洲人和非亚洲人的数据集上采用non-person-disjoint的方式取得99.93%的准确率，采用person-disjoint的方式取得91.94%的准确率；在汉族人和藏族人的数据集上采用non-person-disjoint的方式取得99.69%的准确率，采用person-disjoint的方式取得82.25%的准确率。
结论
2
本文通过数据驱动的方式从训练数据中学习到更适合人种分类的特征，可以很好地实现对基本人种以及亚种族人种的分类，提高了人种分类的精度。同时也首次证明了用虹膜图像进行亚种族分类的可行性，对人种分类理论进行了进一步地丰富和完善。
Objective
2
Iris is the annular part between the pupil and white sclera of human eyes and possesses rich texture information. The iris texture is highly discriminative and stable
which makes iris an important part of the human body for biometric identification. Iris recognition aims to assign a unique identity label to each iris image based on automatic preprocessing
feature analysis
and feature matching. As a reliable method for personal identification
iris recognition has numerous important applications in public and personal security areas. The rapid development of iris recognition in commercial applications has dramatically increased the sizes of iris databases
thereby resulting in large database sizes and slow system responses. Race classification is a key method in solving large-scale iris classification problems. Iris is initially classified according to race
and a rough classification result is obtained. Iris is then matched with the subclass where it belongs. In this way
the runtime for iris recognition can be reduced effectively. Several applications can be adopted for race classification. In the era of information
if a computer can automatically detect the race information of a user
then it can match the computer language with the user language and provide a personalized login interface. Existing approaches to iris-based race classification mainly focus on Asian and non-Asian iris image classification
and the features used for classification are manually designed. Sub-ethnic classification
such as the classification of Koreans
Japanese
and Chinese
has also emerged in recent years. However
no sub-ethnic classification based on iris images has been conducted. No significant difference in iris texture exists among subspecies compared with the iris of the basic race
and manually designed features for basic race are not suited to sub-ethnic classification. These problems pose a great challenge to sub-ethnic classification on iris images. This study proposes a novel race classification method based on deep features and Fisher vectors of iris texture. The study focuses on basic race classification of Asian and non-Asian iris images and sub-ethnic classification of Han and Tibetan iris images.
Method
2
The original iris image contains not only the annular iris but also the pupil
eyelids
eyelashes
and other eye areas
as well as some light reflection of the formation of light spots. Therefore
the iris image should be preprocessed before features are extracted. The iris image preprocessing mainly includes iris detection
localization
segmentation
and normalization. Normalized unwrapped iris images are obtained. Our method feeds the preprocessed iris images to a convolutional neural network to extract deep features as low-level features. We use a Gaussian mixture model to cluster the features to obtain iris texture textons
and the model is then used with Fisher vector to extract high-level features. A support vector machine is used for classification.
Results
2
We evaluate our proposed method on two iris image databases
namely
CASIA multi-race iris race database and Han-Tibetan iris race database
for basic race and sub-ethnic classifications
respectively. Thus far
no iris database dedicated to sub-ethnic classification has been developed. We establish a Han-Tibetan sub-ethnic classification database to further study race classification. We perform an evaluation in two different dataset settings
namely non-person-disjoint and person disjoint. Non-person-disjoint experimental setting refers to the random selection of certain iris images as the training set and the remaining iris images are the test set. In this way
the iris image of the same person can appear in the training and testing sets. Person-disjoint experimental setting means we randomly select iris images of some people as the training set and the iris images of remaining people as the testing set. This method can ensure that the iris images of the same person do not appear in the training and testing sets simultaneously. When we design the experiment
we compare the two methods on the two databases. Experimental results show that the proposed method can achieve 99.93% accuracy in non-person-disjoint approach and 91.94% in person-disjoint manner on the Asian and non-Asian dataset. For the Han-Tibetan dataset
the proposed method can obtain 99.69% accuracy in non-person-disjoint approach and 82.25% accuracy in person-disjoint manner.
Conclusion
2
This study proposes a race classification method based on deep features and Fisher vector of iris texture. The method learns low-level visual features highly suitable for iris race classification from training data to solve the problem of traditional methods
in which a strong prior knowledge is required to design discriminating features. In a data-driven manner
the proposed method can learn features significantly suitable for basic race classification and sub-ethnic classification
which improves the accuracy of race classification. Fisher vector is used to encode low-level visual features. The obtained features can describe the global texture features of iris images
as well as retain local texture features
which is favorable to race classification. We use iris images to solve the sub-ethnic classification of Han and Tibetan for the first time and prove the feasibility and validity of sub-ethnic classification based on iris images. A new iris image database
which is suitable for sub-ethnic classification based on iris images is also established. The experimental results show that the differences in sub-ethnic iris images are insignificant and that the classification is challenging.
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