Gender Classification from Iris Texture Images Using a New Set of Binary Statistical Image Features.

基于一组新的二元统计图像特征的虹膜纹理图像性别分类

Juan Tapia and Claudia Arellano Universidad Tec no logic a de Chile - INACAP j-tapiaf@inacap.cl A pre-print version of the paper accepted at 12th IAPR International Conference on Biometrics.

Juan Tapia 和 Claudia Arellano
智利技术大学 - INACAP
j-tapiaf@inacap.cl
本文为被第12届IAPR国际生物识别会议接受的预印本版本。

Abstract

摘要

Soft biometric information such as gender can contribute to many applications like as identification and security. This paper explores the use of a Binary Statistical Features (BSIF) algorithm for classifying gender from iris texture images captured with NIR sensors. It uses the same pipeline for iris recognition systems consisting of iris segmentation,normalisation and then classification.Experiments show that applying BSIF is not straightforward since it can create artificial textures causing mis classification. In order to overcome this limitation, a new set of filters was trained from eyeimages and different sized filters with padding bands were tested on a subject-disjoint database. A Modified-BSIF(MBSIF）method was implemented.The latter achieved better gender classification results $(94.6%$ and $9l.33%$ for the left and right eye respectively). These results are competitive with the state of the art in gender classification.In an additional contribution,a novel gender labelled database was created and it will be available upon request.

性别等软生物特征信息可以应用于身份识别和安全等许多领域。本文探讨了使用二元统计特征 (BSIF) 算法对近红外 (NIR) 传感器捕获的虹膜纹理图像进行性别分类的方法。该方法采用了与虹膜识别系统相同的流程，包括虹膜分割、归一化和分类。实验表明，直接应用 BSIF 算法并不理想，因为它可能会产生人工纹理，导致分类错误。为了克服这一限制，本文从眼部图像中训练了一组新的滤波器，并在一个不重叠的数据库上测试了不同尺寸的带填充带的滤波器。本文实现了一种改进的 BSIF 方法 (MBSIF)，该方法在性别分类上取得了更好的结果 (左眼和右眼分别为 94.6% 和 91.33%)。这些结果与性别分类领域的最新技术具有竞争力。此外，本文还创建了一个新的性别标注数据库，该数据库将根据请求提供。

1. Introduction

1. 引言

Whenever people log onto computers, access an ATM, pass through airport security, use credit cards, or enter highsecurity areas, their identities need to be verified [5, 6]. There is tremendous interest in reliable and secure identification methods. An active research area of this involves gender classification. Algorithms for automatic gender classification have several applications. They can be used for database binning and retrieval, for intelligent user interfaces or visual surveillance. They can also be used to provide demographic information to improve social services, to facilitate payment methods and for marketing applications in general.

每当人们登录计算机、访问 ATM、通过机场安检、使用信用卡或进入高安全区域时，都需要验证他们的身份 [5, 6]。人们对可靠且安全的身份识别方法有着极大的兴趣。性别分类是这一领域的一个活跃研究方向。自动性别分类算法有多种应用。它们可以用于数据库分类和检索、智能用户界面或视觉监控。它们还可以用于提供人口统计信息以改善社会服务、促进支付方式以及一般的营销应用。

Gender classification based on iris images is promising despite challenging problems presented in terms of image analysis [20, 36, 30]. The human iris is an annular part between the pupil and the white sclera. The iris has an extraordinary structure and includes many interlacing minute features such as freckles, coronas, stripes, furrows, crypts and so on. These visible features, generally called the texture of the iris, are unique to each individual [1, 10, 11]. Research has also shown that the iris is essentially stable throughout a person's life. Furthermore, since the iris is externally visible, iris-based biometrics systems can be non-invasive to their users [10, 11] which is important for practical applications. All these properties (i.e., uniqueness, stability and non-invasive ness) make gender classification suitable and attractive as a complement for achieving highly reliable personal identification.

基于虹膜图像的性别分类在图像分析方面面临挑战，但仍具有前景 [20, 36, 30]。人类虹膜是瞳孔和白色巩膜之间的环形部分。虹膜具有独特的结构，包含许多交错的微小特征，如斑点、日冕、条纹、沟槽、隐窝等。这些可见特征通常被称为虹膜纹理，对每个个体都是独一无二的 [1, 10, 11]。研究还表明，虹膜在人的一生中基本上是稳定的。此外，由于虹膜是外部可见的，基于虹膜的生物识别系统可以对其用户无创 [10, 11]，这对于实际应用非常重要。所有这些特性（即唯一性、稳定性和无创性）使得性别分类作为实现高度可靠个人识别的补充方法既合适又具有吸引力。

In this work a gender classification method is proposed. It uses normalised iris texture information which is codified using MBSIF. The outline of this paper is as follows: Section 2 reviews the state of the art in gender classification methods and describes the BSIF algorithm used in this work. Section 3 describes the pipeline of this work and the challenges faced when implementing MBSIF algorithms. Experimental set-up and the results of gender classification using several class if i ers and MBSIF implementation settings are shown in Section 4. Finally, the conclusions are presented in section 5.

在本研究中，提出了一种性别分类方法。该方法使用了通过MBSIF编码的归一化虹膜纹理信息。本文的结构如下：第2节回顾了性别分类方法的最新技术，并描述了本研究中使用的BSIF算法。第3节描述了本研究的流程以及在实现MBSIF算法时面临的挑战。第4节展示了使用多种分类器和MBSIF实现设置的性别分类实验设置和结果。最后，第5节给出了结论。

2. Related work

2. 相关工作

2.1. Gender Classification

2.1. 性别分类

Human faces provide important visual information for gender classification [6, 37]. Most work done to date on gender classification has involved the analysis of facial images and used different pattern analysis to increase the accuracy of classification [14, 2, 13, 30].

人脸为性别分类提供了重要的视觉信息 [6, 37]。迄今为止，大多数关于性别分类的工作都涉及面部图像的分析，并使用不同的模式分析来提高分类的准确性 [14, 2, 13, 30]。

Previous work on gender classification from iris images has focused on handcrafted feature extraction methods using normalised NIR iris images [22, 36, 20, 3, 16, 9, 33]. Some research has utilised uniform patterns or combined uniform patterns with non-uniform patterns to improve performance [38, 27]. A small number of methods have used Deep Learning on Soft-biometrics such as gender with periocular NIR images [18, 34, 28].

以往关于从虹膜图像中进行性别分类的研究主要集中在使用归一化近红外（NIR）虹膜图像的手工特征提取方法 [22, 36, 20, 3, 16, 9, 33]。一些研究利用均匀模式或将均匀模式与非均匀模式结合以提高性能 [38, 27]。少数方法在软生物特征（如性别）上使用了深度学习，并结合了周围近红外图像 [18, 34, 28]。

Tapia et al. [31] classified gender directly from the same binary iris-code that is used for recognition. They found that relevant information for predicting gender is distributed across the iris, rather than localised in particular concentric bands. Therefore, selected features representing a subset of the iris region can achieve better results than when using the whole iris. They have reported $89%$ correct gender prediction by fusing the best features of iris-code from left and right eyes.

Tapia 等人 [31] 直接从用于识别的相同二进制虹膜代码中分类性别。他们发现，预测性别的相关信息分布在虹膜上，而不是集中在特定的同心带中。因此，选择代表虹膜区域子集的特征比使用整个虹膜时能取得更好的结果。他们报告称，通过融合左右眼虹膜代码的最佳特征，性别预测的正确率达到了 $89%$。

Bobeldyk et al. [4] explored gender-prediction accuracy associated with four different regions from NIR iris images: the extended ocular region, the iris-excluded ocular region, the iris-only region, and the normalised iris-only region. They also used a BSIF texture operator to extract features from these four regions. The ocular region demonstrated its best performance at $85.7%$ , while the normalised or unwrapped images exhibited the worst performance, with an almost $20%$ decrease in performance over the ocular region. A summary of gender classification work is presented in Table 1.

Bobeldyk 等人 [4] 探索了与近红外 (NIR) 虹膜图像中四个不同区域相关的性别预测准确性：扩展的眼部区域、排除虹膜的眼部区域、仅虹膜区域以及归一化的仅虹膜区域。他们还使用了 BSIF 纹理算子从这四个区域中提取特征。眼部区域表现最佳，准确率为 $85.7%$，而归一化或展开的图像表现最差，性能比眼部区域下降了近 $20%$。性别分类工作的总结见表 1。

Table 1. Summary of gender classification methods using eye images. NS: Number of Subjects, I: Iris Images, P: Periocular Images, Th: Thermal, CP: Cellphone Images.

表 1: 使用眼部图像的性别分类方法总结。NS: 受试者数量, I: 虹膜图像, P: 眼周图像, Th: 热成像, CP: 手机图像。

论文	I/P	来源	NS	类型	准确率 (%)
V.Thomas 等人 [36]	I	虹膜	N/A	近红外 (NIR)	75.00
S.Lagree 等人 [20]	I	虹膜	300	近红外 (NIR)	62.17
A. Bansal 等人 [3]	I	虹膜	200	近红外 (NIR)	83.60
J. Tapia 等人 [35]	I	虹膜	1,500	近红外 (NIR)	91.00
M.Fairhurst 等人 [9]	I	虹膜	200	近红外 (NIR)	89.74
J. Tapia 等人 [31]	I	虹膜	1,500	近红外 (NIR)	89.00
D. Bobeldyk 等人 [4]	I/P	虹膜	1,083	近红外 (NIR)	85.70
Kuehlkamp 等人 [18]	I/P	虹膜	1,500	近红外 (NIR)	80.00
J. Tapia [33]	I/P	虹膜	1,500	近红外 (NIR)	79.33
J. Tapia 等人 [34]	I	虹膜	1,500	近红外 (NIR)	83.00
J. Merkow 等人 [21]	P	面部	936	可见光 (VIS)	80.00
C.Chen 等人 [8]	P	面部	1,003	近红外/热成像 (NIR/Th)	93.59
Castrillon-Santana 等人 [7]	P	面部	1,500	可见光 (VIS)	92.46
Rattani 等人 [26]	P	虹膜	550	可见光/手机图像 (VIS/CP)	91.60
J. Tapia 等人 [32]	P	虹膜	120/120	近红外/可见光 (NIR/VIS)	90.00

2.2. Binary Statistical Image Feature (BSIF)

2.2. 二值统计图像特征 (Binary Statistical Image Feature, BSIF)

BSIF [16] is a local descriptor constructed by binarising the responses to linear filters. In contrast to previous binary descriptors, the filters learn from thirteen natural images using independent component analysis (ICA). The code value of pixels is considered as a local descriptor of the image intensity pattern in the pixels’ surroundings. The value of each element (i.e bit) in the binary code string is computed by binarising the response of a linear filter with a zero threshold. Each bit is associated with a different filter, and the length of the bit string determines the number of filters used. The set of filters is learned from a training set of natural image patches by maximising the statistical independence of the filter responses [15](See Figure 1). The details of the parameters learned by the linear filters are described below: Given an image patch $X$ of size $l\times l$ pixels and a linear filter $W_{i}$ of the same size, the filter responses $s_{i}$ are obtained by:

BSIF [16] 是一种通过二值化线性滤波器响应构建的局部描述符。与之前的二值描述符不同，这些滤波器通过独立成分分析 (ICA) 从十三张自然图像中学习得到。像素的编码值被视为像素周围图像强度模式的局部描述符。二进制代码字符串中每个元素（即比特）的值通过将线性滤波器的响应以零阈值二值化计算得到。每个比特与不同的滤波器相关联，比特串的长度决定了使用的滤波器数量。滤波器组通过最大化滤波器响应的统计独立性从自然图像块的训练集中学习得到 [15]（见图 1）。线性滤波器学习的参数细节如下：给定一个大小为 $l\times l$ 像素的图像块 $X$ 和一个相同大小的线性滤波器 $W_{i}$，滤波器响应 $s_{i}$ 通过以下方式获得：

Where, vector notation is introduced in the latter stage, for instance the vector $w$ and $x$ contain the pixels of $W_{i}$ and $X$ . The binarised feature $b_{i}$ is obtained by setting $b_{i}=1$ if $s_{i}>0$ and $b_{i}=0$ otherwise. Given $n$ linear filters $W_{i}$ ,we may stack them to a matrix $W$ of size $n\times l^{2}$ and compute all responses at once, i.e. $s=W x$ . We obtain the bit string $b$ by binarising each element $s_{i}$ of $s$ as above. Thus, given the linear feature detectors $W_{i}$ , computation of the bit string $b$ is straightforward. Also, it is clear that the bit strings for all image patches of size $l\times l$ , surrounding each pixel of an image can be computed conveniently by $n$ convolutions.

在后续阶段引入了向量表示法，例如向量 $w$ 和 $x$ 分别包含 $W_{i}$ 和 $X$ 的像素。二值化特征 $b_{i}$ 通过以下方式获得：如果 $s_{i}>0$ ，则设置 $b_{i}=1$ ，否则 $b_{i}=0$ 。给定 $n$ 个线性滤波器 $W_{i}$ ，我们可以将它们堆叠成一个大小为 $n\times l^{2}$ 的矩阵 $W$ ，并一次性计算所有响应，即 $s=W x$ 。我们通过如上所述对 $s$ 的每个元素 $s_{i}$ 进行二值化来获得比特串 $b$ 。因此，给定线性特征检测器 $W_{i}$ ，计算比特串 $b$ 是直接的。此外，显然可以通过 $n$ 次卷积方便地计算图像中每个像素周围大小为 $l\times l$ 的所有图像块的比特串。

The final image is obtained by:

最终图像通过以下方式获得：

Where, C'odeIm is an accumulative image, $C r$ is the convolution between the filter and the image that is later binarised and multiplied by the number of bits. For instance, if we use 9 bits then we compute CodeIm for $2^{1}$ later for $2^{2}$ up to $2^{9}$ . The final image will be the sum of the 9 images for each CodeIm.

其中，C'odeIm 是累积图像，$C r$ 是滤波器与图像之间的卷积，随后进行二值化并乘以位数。例如，如果我们使用 9 位，那么我们首先计算 $2^{1}$ 的 CodeIm，然后是 $2^{2}$，直到 $2^{9}$。最终图像将是每个 CodeIm 的 9 张图像的总和。

BSIF have been used for several applications including biometrics from iris images [17, 12, 24]. In this work, a gender classification algorithm using normalised NIR iris images is proposed. It uses a similar pipeline than iris recognition systems. The iris is segmented and occlusions are masked. BSIF can be sensitive to image boundaries and the occlusion mask creating artificial texture which may mislead gender classification results.

BSIF 已被用于多种应用，包括基于虹膜图像的生物识别 [17, 12, 24]。本文提出了一种使用归一化近红外 (NIR) 虹膜图像的性别分类算法。它采用了与虹膜识别系统类似的流程。虹膜被分割，遮挡部分被掩码。BSIF 对图像边界和遮挡掩码可能敏感，这些可能会产生误导性别分类结果的人工纹理。

This paper explores a new set of filters (See Figure 1) trained from thirteen eye images instead of natural images as used in traditional approach. The influence of the filter size, the padding (boundaries) and the number of bits used when implementing MBSIF algorithm are also explored.

本文探讨了一组新的滤波器（见图 1），这些滤波器是从 13 张眼睛图像中训练出来的，而不是传统方法中使用的自然图像。同时还探讨了滤波器大小、填充（边界）以及在实现 MBSIF 算法时使用的比特数的影响。

3. Gender classification using BSIF

3. 使用 BSIF 进行性别分类

This paper proposes the use of the same pipeline that is used for iris recognition systems. The input image is segmented in a pre-process step. The iris region is then transformed to a polar space and codified using MBSIF. Finally, gender classification is performed using a new database and several class if i ers (Section 3.3).

本文提出使用与虹膜识别系统相同的流程。输入图像在预处理步骤中进行分割。然后，虹膜区域被转换到极坐标空间，并使用 MBSIF 进行编码。最后，使用一个新的数据库和多个分类器进行性别分类（第 3.3 节）。

3.1. Iris Segmentation and Normalisation

3.1. 虹膜分割与归一化

The iris is detected from the input image using commercial software Osiris [23]. A segmentation mask occludes the eyelids, eyelashes and specular refection portions of the iris image which are not useful for gender classification. It is important to note that iris images of different persons, or even the left and right iris images for a given person, may not present exactly the same mask and imaging conditions (see Figure 2). Ml lumi nation by LEDs during capture may come from either side of the sensor, specular highlights may be present in different places in the image. Eyelid and head position may also affect segmentation.

使用商业软件 Osiris [23] 从输入图像中检测虹膜。分割掩码遮挡了虹膜图像中无助于性别分类的眼睑、睫毛和镜面反射部分。需要注意的是，不同人的虹膜图像，甚至同一个人的左右眼虹膜图像，可能不会呈现完全相同的掩码和成像条件（见图 2）。在捕捉过程中，LED 的照明可能来自传感器的任一侧，镜面高光可能出现在图像的不同位置。眼睑和头部位置也可能影响分割。

Figure 1. Left, Example of patches extracted from natural images for traditional BSIF. Right, Example of patches extracted from Eye mages for Modified BSIF.

图 1: 左图，传统 BSIF 从自然图像中提取的补丁示例。右图，改进的 BSIF 从眼部图像中提取的补丁示例。

The segmented iris is normalised or unwrapped with radial $(r)$ and angular $(\theta)$ resolutions which determine the size of the rectangular iris image. The size of the normalised iris can significantly influence the iris recognition rate. In this work, a rectangular image of $20(r)\mathbf{x}~240(\theta)$ created using Osiris software [23] with automatic segmentation is used for all experiments.

分割后的虹膜通过径向 $(r)$ 和角度 $(\theta)$ 分辨率进行归一化或展开，这些分辨率决定了矩形虹膜图像的大小。归一化虹膜的大小会显著影响虹膜识别率。在本研究中，使用 Osiris 软件 [23] 自动分割生成的 $20(r)\mathbf{x}~240(\theta)$ 矩形图像用于所有实验。

Figure 2. Two original images from right and left eye (a). Segmented and masked images with eyelid and eyelash detection using Osiris (b). Images (c) and (d) are normalised images from the right and left eye both with the mask in yellow.

图 2: 来自右眼和左眼的两张原始图像 (a)。使用 Osiris 进行眼睑和睫毛检测后的分割和掩码图像 (b)。图像 (c) 和 (d) 是来自右眼和左眼的归一化图像，均带有黄色掩码。

3.2. BSIF filters application

3.2. BSIF 滤波器应用

BSIF filters compute the convolution with each normalised masked image. Each filter represents a different pattern. The final image is the results of all previous images binarised by $2^{n}$ bits. The best filter size is one that represents the correct size of the mask with the lowest number of bits. If the filter is smaller than the mask, then artificial texture information will be created and the resulting image will not well represent its original information. On the other hand, if the mask of the iris is larger than the filter, a flat area will be obtained and the filter will need to be adjusted by reducing its size. Since the size of the normalised iris imageis $20\times240$ ,special care needs to be taken in order to minimise the effects of boundary and its influence on filter size. A common approach to dealing with border effects is to pad the original image with extra rows and columns based on your filter size.

BSIF 滤波器计算与每个归一化掩码图像的卷积。每个滤波器代表不同的模式。最终图像是所有先前图像通过 $2^{n}$ 位二值化的结果。最佳滤波器大小是能够以最低位数正确表示掩码大小的滤波器。如果滤波器小于掩码，则会创建人工纹理信息，生成的图像将无法很好地表示其原始信息。另一方面，如果虹膜的掩码大于滤波器，则会获得平坦区域，滤波器需要通过减小其大小进行调整。由于归一化虹膜图像的大小为 $20\times240$ ，因此需要特别注意以最小化边界效应及其对滤波器大小的影响。处理边界效应的常见方法是根据滤波器大小在原始图像周围添加额外的行和列。

Traditional implementation of BSIF increases the size of the image and wraps the filter around it. Unfortunately, this implementation directly affects the results of the binarised iris image. Figure 3 (A) shows an example where this implementation is used. The first row (a), shows the normalised iris image obtained directly from Osiris software [23]. The second row (b) shows the extra rows added through the wrapping process. A $5\times240$ pixel band is added to the top and bottom of the original image. Addit ional bands of $5\times20$ pixels are added to the vertical sides of the image (left and right). Note that the horizontal band added to the top of the image represents the bottom of the original image (mask area) and, the horizontal band added to the bottom of the image represents the top of the original image (Figure 3, column (A), row (b)). This implementation directly affects the resulting binarised image since the boundary added creates artificial texture as can be seen in the resulting images in Figure 3, column (A), row (d).

传统 BSIF 的实现会增加图像的尺寸并将滤波器环绕在图像周围。不幸的是，这种实现方式会直接影响二值化虹膜图像的结果。图 3 (A) 展示了使用这种实现方式的示例。第一行 (a) 显示了直接从 Osiris 软件 [23] 中获取的归一化虹膜图像。第二行 (b) 展示了通过环绕过程添加的额外行。在原始图像的顶部和底部添加了一个 $5\times240$ 像素的带。在图像的垂直侧（左侧和右侧）添加了 $5\times20$ 像素的额外带。请注意，添加到图像顶部的水平带代表原始图像的底部（掩码区域），而添加到图像底部的水平带代表原始图像的顶部（图 3，列 (A)，行 (b)）。这种实现方式会直接影响生成的二值化图像，因为添加的边界会创建人工纹理，如图 3 列 (A) 行 (d) 中的结果图像所示。

A alternative way to deal with border effects is to pad the original image with zeros (Or a constant value), reflecting the image at the borders or replicating the first and last row/column as many times as needed

处理边界效应的另一种方法是用零（或常数值）填充原始图像，在边界处反射图像或根据需要多次复制第一行/列和最后一行/列。

Figure 3. Column (A) shows an example of traditional BSIF implementation. (a) corresponds to the input normalised iris image with the mask information in yellow. (b) illustrates padding implemented were bands 1 and 2 are wrapped on the image and (c) the resulting image after applying BSIF flters (11x11 pixels and 9 bits). A similar example is shown in column (B). In this case two bands of pixels (1 and 2) are replicated at the top and bottom of the image. The resulting image in (d) replicates the mask of the input image without adding extra artificial texture

图 3: 列 (A) 展示了传统 BSIF 实现的示例。(a) 对应输入归一化的虹膜图像，黄色部分为掩码信息。(b) 展示了在图像上实现的填充，其中波段 1 和 2 被包裹在图像上，(c) 是应用 BSIF 滤波器 (11x11 像素和 9 位) 后的结果图像。列 (B) 展示了一个类似的示例。在这种情况下，图像顶部和底部复制了两个像素波段 (1 和 2)。(d) 中的结