Fully Connected Networks and Generative Neural Networks Applied to Sclera Segmentation

Fully Connected Networks and Generative Neural Networks
Applied to Sclera Segmentation

Diego R. Lucio, Rayson Laroca, Evair Severo, Alceu S. Britto Jr., David Menotti
Federal University of Paraná (UFPR), Curitiba, PR, Brazil
Pontifical Catholic University of Paraná (PUCPR), Curitiba, PR, Brazil
{drlucio,rblsantos,ebsevero,menotti}@inf.ufpr.br alceu@ppgia.pucpr.br
Abstract

Due to the world’s demand for security systems, biometrics can be seen as an important topic of research in computer vision. One of the biometric forms that has been gaining attention is the recognition based on sclera. The initial and paramount step for performing this type of recognition is the segmentation of the region of interest, i.e. the sclera. In this context, two approaches for such task based on the Fully Connected Network (FCN)and on Generative Adversarial Network (GAN)are introduced in this work. FCNis similar to a common convolution neural network, however the fully connected layers (i.e., the classification layers) are removed from the end of the network and the output is generated by combining the output of pooling layers from different convolutional ones. The GANis based on the game theory, where we have two networks competing with each other to generate the best segmentation. In order to perform fair comparison with baselines and quantitative and objective evaluations of the proposed approaches, we provide to the scientific community new , manually segmented images from two databases111The new sclera segmentation annotations are publicly available to the research community at https://web.inf.ufpr.br/vri/databases/sclera-segmentation-annotations/.. The experiments are performed on the UBIRIS.v2 and MICHE databases and the best performing configurations of our propositions achieved - measures of and , respectively.

1 Introduction

In recent years, the interest in using biometrics to automatically identify and/or verify a person’s identity has greatly increased. Many characteristics can be used to identify a person, such as physical, biological and behavioral traits [7, 32]. Biometrics are especially important as they can not be changed, forgotten, lost or stolen, providing an unquestionable connection between the individual and the application that makes use of it [4].

Several characteristics of the human body can be used as biometrics, such as fingerprint, face, iris, retina and voice, each one with its advantages and disadvantages. The iris and retina are among the most accurate biometrics [7]. However, biometric systems based on iris and retina having high degree of reliability require, respectively, user collaboration and an intrusive image acquisition scheme [8]. In addition to the iris and retina biometric traits, the eye has also a white region around the eyeball known as sclera that contains a pattern of blood vessels that can be used for personal identification [9, 6, 11].

Typically, segmentation is the first step in which efforts should be applied in a reliable sclera-based recognition system. Incorrect segmentation can either reduce the region of blood vessels or introduce new patterns such as eyelashes and eyelids, impairing the system effectiveness.

In order to avoid the above mentioned problems and to encourage the creation of new sclera segmentation techniques, some competitions were performed [8, 9, 6]. The state of the art method in these competitions was obtained using a neural network technique named autoencoder in the Multi-Angle Sclera Dataset.v1 (MASD.v1)database. When images of a single sensor were used, the best attained recall and precision rates were % and %, respectively.

In this work, we proposed two new approaches to sclera segmentation based on Convolutional Neural Networks (CNNs), one based on Fully Connected Network (FCN) [31] and another one based on Generative Adversarial Network (GAN) [16]. To the best of our knowledge, both kinds of networks have never been studied in the sclera segmentation scenario. FCNis used for segmentation in a large range of applications, from medical to satellite image analysis [14, 29], while GANis a new approach to semantic segmentation, which has outperformed the state of the art [21]. The results yielded by the proposed approaches outperform the ones of previous works on a subject of UBIRIS.v2 database [25]. This subject contains 201 images kindly provided by the authors of [12] and more 300 ones manually labeled by us. We also present promising results for sclera segmentation on a subset (1000 images) of the Mobile Iris Challenge Evaluation I (MICHE-I)database [22], which was never been used in this context and initially proposed for iris segmentation and recognition from mobile images. For producing a fair and quantitative comparison among proposed approaches and a baseline one (SegNet [3]), we manually labeled 1,300 images from those databases, making the masks publicly available for research purposes. Regarding the MASD.v1database which was the focus of the previous mentioned competitions for sclera segmentation, we do not have yet implementations on Matlab as required by the organizers of those competitions.

The main contributions of this paper can be summarized as follows: 1) Two new approaches for sclera segmentation; 2) A comparative evaluation of the proposed approaches with a baseline one; 3) Two datasets composed of , sclera images manually labeled, being , from the MICHE-Idatabase and from the UBIRIS.v2database.

The remainder of this paper is organized as follows: we briefly review the related work in Section 2. In Section 3, the proposed segmentation approach is described. Section 4 and 5 present the experiments and the results obtained, respectively. Finally, conclusions and future work are discussed in Section 6.

2 Related Work

In this section, we present a review of the most relevant studies in the sclera segmentation context. We start by presenting some relevant sclera recognition methods in which different sclera segmentation techniques were proposed. Finally, we describe some important works specially dedicated to sclera segmentation techniques.

2.1 Sclera-based Recognition Methods

The works presented in this subsection employed sclera segmentation as preprocessing for sclera-based recognition. Note that, in such cases, the authors did not report the [precision, recall, F-score] achieved in the segmentation stage.

Zhou et al. [32] proposed a new concept for human identification based on the pattern of sclera vessels. In their work, they presented a fully automated sclera segmentation approach for both color and grayscale images. In color images, the sclera region is estimated using the best representation between two color-based techniques. On the other hand, the Otsu’s thresholding method is applied to find the sclera region in grayscale images. The UBIRIS.v1 [24] and IUPUI [32] databases were used in the experiments.

Das et al. [7] presented a methodology where the right and left sclera are segmented separately. The time adaptive active contour-based region growing segmentation technique proposed by Chan & Vese [5] was employed. The authors applied the Daugman’s integro-differential operator to find the seed point required in the region growing-based segmentation [10]. The UBIRIS.v1database was used in the experiments.

Delna et al. [11] presented a sclera identification based on a single-board computer. The sclera region is segmented as a rectangle from the Hough circular transform applied for iris location. All images used in the experiments were obtained from a webcam connected to a Raspberry Pi.

2.2 Sclera Segmentation Techniques

Das et al. [8] presented a benchmark for sclera segmentation where four research groups proposed their solutions for this task. All approaches were evaluated in the MASD.v1database, proposed in the competition. The best results were obtained by Team 4 [27], where the authors presented a novel sclera segmentation algorithm for color images which operates at pixel-level. Exploring various color spaces, the proposed approach is robust to image noise and different gaze directions. The algorithm’s robustness is enhanced by a two-stage classifier. At the first stage, a set of simple classifiers is employed, while at the second stage, a neural network classifier operates on the probabilities space generated by the classifier at the first stage. The reported precision and recall rates were % and %, respectively, on the MASD.v1database.

Das et al. [9] proposed a new benchmark, which addresses sclera segmentation and recognition. The best segmentation results reached % precision and % recall. This result is achieved using a method based on Fuzzy C Means, which considers spatial information and uses Gaussian kernel function to calculate the distance between the center of the cluster and the data points.

Alkassar et al. [1] proposed a segmentation algorithm which fuses multiple color space skin classifiers to overcome noise introduced by sclera acquisition, such as motion, blur, gaze and rotation. This approach was evaluated using the UBIRIS.v1, UBIRIS.v2 [25] and UTIRIS [15] databases.

Das et al. [6] presented a new sclera segmentation benchmark where seven research groups proposed their solutions for this task. The best results attained precision and recall rates of % and %, respectively. These results were obtained by using a neural network architecture based on the encoder-decoder approach called SegNet [3].

2.3 Final Remarks

As one may see, the use of CNNswas not much explored in the challenging sclera segmentation task. Thus, one of the contributions of this paper is to explore this aspect in the attempt of obtaining improvements in terms of sclera segmentation accuracy.

3 Proposed Approach

Some images might present specular highlights in regions of the subject’s face. In our preliminary tests, many of these regions were erroneously classified as sclera. Therefore, we propose to first locate the periocular region and then perform the sclera segmentation in the detected patch.

This section describes the proposed approach and it is divided into two subsections, one for Periocular Region Detection (PRD)and one for sclera segmentation.

3.1 Periocular Region Detection

YOLO [28] is a object detection framework based on CNN, which regards detection as a regression problem. As great advances were recently achieved through YOLO-inspired models[18, 23], we decided to fine-tune it for PRD. However, as we want to detect only one class and the computational cost is one of our main concerns, we chose to use a smaller model, called Fast-YOLO222For training Fast-YOLO we used the weights pre-trained on ImageNet, available at https://pjreddie.com/darknet/yolo/ [28], which uses fewer convolutional layers than YOLO and fewer filters in those layers. The Fast-YOLO’s architecture is shown in Table 1.

Layer Filters Size Input Output
conv
max
conv
max
conv
max
conv
max
conv
max
conv
max
conv
conv
conv
detection
Table 1: Fast-YOLO network used to detect the periocular region. We reduced the number of filters in the last convolutional layer from to in order to output class instead of .

The PRDnetwork is trained using the images, without any preprocessing, and the coordinates of the Region of Interest (ROI)as inputs. As ground truth, we used the annotations provided by Severo et al. [30]. As these annotations were made for iris location, we applied a padding (chosen based on the validation set) in the detected patch (i.e., iris), so that the sclera is entirely within the ROI.

By default, Fast-YOLO only returns objects detected with a confidence of or higher. We consider only the detection with the largest confidence in cases where more than one periocular region is detected, since there is always only one region annotated in the evaluated databases. If no region is detected, the next stage (sclera segmentation) is performed on the image in its original size.

3.2 Sclera Segmentation

We employ three approaches for sclera segmentation, since they presented good results in other segmentation applications. These approaches are: FCN, Encoder-Decoder (ED)and GAN. Its noteworthy that EDwas employed for sclera segmentation in [6], obtaining state-of-the-art results. Therefore, we made use of EDin the databases used in this paper, considering it as the baseline for comparison with the proposed approach.

3.2.1 Fully Connected Network

This segmentation approach was proposed by Long et al. [20]. The network has only convolutional layers and the segmentation process can take input images of arbitrary sizes, producing correspondingly-sized output with efficient inference and learning.

In this work, we employ the FCNapproach presented by Teichmann et al. [31]. As shown in Figure 1, features are extracted using a CNNwithout the fully connected layers (i.e., VGG- without the last layers).

Figure 1: FCNarchitecture for sclera segmentation.

Next, the extracted features pass through two convolutional layers, generating an output of dimension . The output of these convolutional layers is processed by the FCN8 architecture proposed in [20], which performs the up-sampling combining the last three layers from the VGG-.

3.2.2 Encoder-Decoder

Layer Filters Size Input Output Layer Filters Size Input Output
enc 64 up
enc 64 dec 512
max dec 512
enc 128 dec 512
enc 128 up
max dec 512
enc 256 dec 512
enc 256 dec 256
enc 256 up
max dec 256
enc 512 dec 256
enc 512 dec 128
enc 512 up
max dec 128
enc 512 dec 64
enc 512 up
enc 512 dec 64
max dec 2
Table 2: SegNet architecture.

A convolutional ED, also called autoencoder, is a neural network trained in order to copy its input to the output. The purpose is to learn data encoding which can be used for dimensionality reduction or even for file compression [2].

The ED(SegNet) used in this work was presented in [3]. SegNet consists of a stack of encoders followed by a corresponding stack of decoders which feed a soft-max classification layer. Decoders map low-resolution features extracted by encoders to an image with the same dimension as the input. The architecture used is presented in Table 2.

3.2.3 Generative Adversarial Network

GANsare deep neural networks composed by both generator and discriminator networks, pitting one against the other. First, the generator network receives noise as input and generates samples. Then, the discriminator network receives samples of training data and those of the generator network, being able to distinguish between the two sources [13]. A generic GAN architecture is shown in Figure 2.

Figure 2: GANarchitecture for sclera segmentation.

Basically, the generator network learns to produce more realistic samples throughout each iteration, and the discriminator network learns to better distinguish real and synthetic datas.

Isola et al. [16] presented the GANapproach used in this work, which is a conditional GANable to learn the relation between a image and its label file, and, from that, generate a variety of image types, which can be employed in various tasks such as photo generation and semantic segmentation.

4 Experiments

In this section, we present the databases and the evaluation protocol used in our experiments.

4.1 Databases

The experiments were carried out in two subsets of well-known iris databases: UBIRIS.v2and MICHE-I. An overview of both subsets can be seen in Table 3. Remark that we do not use the SSBC [8] and SSRBC [9] databases in our experiments as only the test sets were made available by the authors.

Database Images Subjects Resolution
UBIRIS.v2
MICHE-I , Various
MICHE-GS4 Various
MICHE-IP5 Various
MICHE-GT2
Table 3: Overview of the databases used in this work. All of these are a subset of the original database.

UBIRIS.v2: this database is composed of , images collected from both eyes from subjects and have a resolution of pixels.

MICHE-I: this database consists of , images captured from subjects under uncontrolled settings using three mobile devices: iPhone 5, Galaxy Samsung IV and Galaxy Tablet II (,, , and images, respectively), with many different resolutions [22].

4.2 Preprocessing

As discussed in Section 3.1, it was necessary to first detect the periocular region as some images present specular highlights, impairing the performance of the system. After detecting the periocular region, only the ROIwas maintained in each image, providing a great improvement over the results obtained at first. Figure 3 shows an example of each subset of the original image (without PRD) and the segmentation mask created by us.

(a)
(b)
(c)
(d)
Figure 3: Four examples of the masks created by us.

Figure 4 shows, instead, four cropped images after PRDand their respective masks. It is noteworthy that most specular highlights are removed after PRD.

(a)
(b)
(c)
(d)
Figure 4: Periocular regions detected and replicated to the masks.

At last, the ROIis resized according to each approach proposed in Section 3. The input sizes were chosen based on the original architectures of the approaches (see Table 4).

Approach Image - Dimension Mask - Dimension
FCN
GAN
SegNet
Table 4: Image dimensions used in each approach.

4.3 Evaluation Protocol

The performance evaluation of an automatic segmented mask is performed in a pixel-to-pixel comparison between the ground truth and the predicted image. Therefore, we use the following metrics: Precision, Recall and F-score.

To perform a fair evaluation and comparison of the proposed approaches in all databases, we divided each into three subsets, being of the images for training, for testing and for validation.

5 Results and Discussions

The experiments were carried out using the protocol presented in Section 4.3. We also performed some additional experiments using the cross-sensor methodology.

5.1 Proposed Protocol

The results obtained by both the baseline (SegNet) and the proposed approaches are shown in Table 5. The baseline presented considerably worse results. We believe this is due to the size of the training set, since SegNet was originally employed in a large dataset [27]. Radu et al. [27] generated a dataset with , images using data augmentation. However, we did not have access to the database used by them, and thus a more direct comparison with their methodology was not possible to be done.

Database Approach Recall % Precision % F-score %
UBIRIS.v2 GAN
SegNet
FCN 87.31 06.68 88.45 06.98 87.48 03.90
MICHE-I GAN
SegNet
FCN 87.59 11.28 89.90 09.82 88.32 09.80
MICHE-GS4 GAN
SegNet
FCN 88.24 12.03 88.65 10.62 88.12 10.56
MICHE-IP5 GAN
SegNet
FCN 87.51 11.61 89.32 05.22 87.80 08.24
MICHE-GT2 GAN
SegNet
FCN 87.86 12.23 88.50 12.68 87.94 11.59
Table 5: Results achieved using the proposed protocol.

Better results were obtained using the proposed approaches. In the UBIRIS.v2subset, the GAN-based sclera segmentation attained a F-score value of , while the approach based on FCNachieved . Although there is little difference between the F-score values obtained by both methods, the standard deviation presented when using FCNwas slightly lower than when GANwas employed for the segmentation.

The same happened in all subsets used in our experiments, fact that makes us believe that the FCNapproach is best suited for sclera segmentation. However, the results obtained with the GAN-based segmentation should not be diminished, since they were very close to the best results.

Here we perform a visual analysis. For this task, we randomly chose an image from the UBIRIS.v2subset. Figures LABEL:fig:masksa, LABEL:fig:masksb and LABEL:fig:masksc demonstrate a very poor outcome in the segmentation of the sclera. As can be seen, the FCNapproach presented a considerably better segmentation result when compared to the baseline and GAN. The same occurs in many other images, but the results are not always so discrepant (see Figures LABEL:fig:masksd, LABEL:fig:maskse and LABEL:fig:masksf). It is noteworthy the consistency presented with FCN-based segmentation technique, observed in all sclera images generated in this work.

(a)
(b)
(c)
(d)
(e)
(f)
Figure 5: Samples of segmented scleras using the ground truth for highlighting errors: green and red pixels represent the FPs and FNs respectively.

5.2 Additional Experiments Using Cross-Sensor

In this section, we present the results obtained using a cross-sensor methodology, where two experiments were performed. In the first one, we used MICHE-I(see Table 3) as training set and UBIRIS.v2as test set. In the second experiment, we inverted the order and used UBIRIS.v2as training set and MICHE-Ias test set.

Table 6 presents the results obtained using MICHE-Ias training set and UBIRIS.v2as test set. As we can see, the obtained F-score was very close to that obtained when the training and test sets were from the same database, reaching a F-score value % higher. However, in this case the best segmentation was achieved with the GAN-based approach.

Approach Recall % Precision % F-score %
GAN 90.02 05.46 85.96 07.90 87.52 03.74
SegNet
FCN
Table 6: Results obtained using MICHE-Ias training set and UBIRIS.v2as test set.

The same did not happen when we used UBIRIS.v2as training set and MICHE-Ias test set, as shown in Table 7. The attained F-score values were much lower than those obtained when MICHE-Iwas used as training.

Approach Recall % Precision % F-score %
GAN
SegNet
FCN
Table 7: Results obtained using UBIRIS.v2as training set and MICHE-Ias test set.

This might have occurred because the subset of the MICHE-Idatabase used in this work has more diversity and it is larger than the UBIRIS.v2subset, which allows the generated model to better discriminate the pixels belonging to sclera.

6 Conclusions and Future Work

This work introduced two new approaches for sclera segmentation and compared them with a baseline (SegNet) method chosen in the literature. Both proposed approaches (FCNand GAN) attained higher precision and recall values in all evaluated scenarios. Furthermore, these approaches presented promising results when evaluated in cross-sensor scenarios.

We also manually labeled images for sclera segmentation. These masks will be available to the research community once this work is accepted for publication, assisting in the fair comparison among published works.

There is still room for improvements in sclera segmentation, so we intend to: 1) design new and better network architectures; 2) create a unique architecture that integrates the detection stage of the periocular region; 3) employ a post-processing stage to refine the segmentation given by the proposed approaches; 4) design a general and independent sensor approach, where firstly the image sensor is classified and then the sclera is segmented with a specific approach; 5) compare the proposed approaches with methods applied in other domains such as iris segmentation [19, 17] and periocular-based recognition [26].

Acknowledgments

The authors thank the National Council for Scientific and Technological Development (CNPq) (# 428333/2016-8 and # 313423/2017-2) and the Coordination for the Improvement of Higher Education Personnel (CAPES) for the financial support. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

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