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The circular characteristic of the eyes makes it easy to use in a matching algorithm. In this study, images are reduced to black and white, and then a circle is used as a template for matching. Even though eyes are not fully circle, but an ellipse with a ratio of 1.13 usually this difference is negligible because cameras do not have the necessary resolution to catch that difference. However, in a state where the camera has a good resolution to catch that difference, an ellipse must be used as a template instead of a circle. A simple input image which will be used in template matching is created using an estimated radius value as seen in Fig. 5. However, this approach does not provide satisfactory results.

Figure 5 The circular characteristic of the eyes makes it easy to use in a matching algorithm. In this study, images are reduced to black and white, and then a circle is used as a template for matching. Even though eyes are not fully circle, but an ellipse with a ratio of 1.13 usually this difference is negligible because cameras do not have the necessary resolution to catch that difference. However, in a state where the camera has a good resolution to catch that difference, an ellipse must be used as a template instead of a circle. A simple input image which will be used in template matching is created using an estimated radius value as seen in Fig. 5. However, this approach does not provide satisfactory results.

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Figure 1 Three main steps of eye finding algorithms which use Haar-Like cascades The purpose of detecting the face is to ensure that the search area has limited characteristics and appearances. There should not be objects or images like the eye in the search area. Also by reducing the area, the workload of the algorithm can be reduced. Face detection is a decade-old phenomenon, and there are many methods to solve this [20]. In literature, many choose the Viola-Jones method to detect the faces by using Haar-Like cascades. According to the tests carried out on the BioID database, this method manages to successfully detect faces in 1365 images out of 1521 images which means over 89% success rate. There are only eleven false positives out of 1521 images. These false positive results are usually a smaller region within a face so it can be easily cast off by choosing the biggest face from detected faces. For TalkingFace database success rate is 99.1%.
Figure 1 Three main steps of eye finding algorithms which use Haar-Like cascades The purpose of detecting the face is to ensure that the search area has limited characteristics and appearances. There should not be objects or images like the eye in the search area. Also by reducing the area, the workload of the algorithm can be reduced. Face detection is a decade-old phenomenon, and there are many methods to solve this [20]. In literature, many choose the Viola-Jones method to detect the faces by using Haar-Like cascades. According to the tests carried out on the BioID database, this method manages to successfully detect faces in 1365 images out of 1521 images which means over 89% success rate. There are only eleven false positives out of 1521 images. These false positive results are usually a smaller region within a face so it can be easily cast off by choosing the biggest face from detected faces. For TalkingFace database success rate is 99.1%.
Figure 3 The numeric markup for landmark points According to the tests carried out on the BioID database, the facial landmarks successfully detect the faces in the 1509 images from 1521 images, which means more than 99% success rate. According to the test carried out on the TalkingFace database, the facial landmarks successfully detect all faces. There are five false positives in TalkingFace database. However, like the Viola-Jones method, these false positive results are usually a smaller region within a detected face, thus it can be easily discarded by choosing the biggest face from detected faces. The ColorFeret database success rate is also 100%. As seen in the results; using facial landmarks has a much better success rate than the Viola-Jones method.
Figure 3 The numeric markup for landmark points According to the tests carried out on the BioID database, the facial landmarks successfully detect the faces in the 1509 images from 1521 images, which means more than 99% success rate. According to the test carried out on the TalkingFace database, the facial landmarks successfully detect all faces. There are five false positives in TalkingFace database. However, like the Viola-Jones method, these false positive results are usually a smaller region within a detected face, thus it can be easily discarded by choosing the biggest face from detected faces. The ColorFeret database success rate is also 100%. As seen in the results; using facial landmarks has a much better success rate than the Viola-Jones method.
Figure 4 Created eye region using landmarks and the natural rate of eye and distance between two eye corners
Figure 4 Created eye region using landmarks and the natural rate of eye and distance between two eye corners
Figure 7 Reduced window movement Considering characteristics of the eye, detecting eye posi of th wind ime ion in a windowed manner within an eye region may be unnecessarily time-consuming. Also, as the resolution e eye region grows, the time needed to compute every ow would also grow. In order to further reduce the consumption, instead of checking all of the windows, he two-stage window movement is tried as seen in Fig. 7. “Or best he starting position, the middle of the y-axis of the eye region is chosen as a center of the y-axis of the window. After finding the best x-axis position for given y-axis; using X-axis position the window is moved in y-axis to determine best overall position. 2.4 Matter of Blink
Figure 7 Reduced window movement Considering characteristics of the eye, detecting eye posi of th wind ime ion in a windowed manner within an eye region may be unnecessarily time-consuming. Also, as the resolution e eye region grows, the time needed to compute every ow would also grow. In order to further reduce the consumption, instead of checking all of the windows, he two-stage window movement is tried as seen in Fig. 7. “Or best he starting position, the middle of the y-axis of the eye region is chosen as a center of the y-axis of the window. After finding the best x-axis position for given y-axis; using X-axis position the window is moved in y-axis to determine best overall position. 2.4 Matter of Blink
Using all points within the circle has a good success rate. However, in order to further reduce the workload of the method instead of using all points within a circle reduced points are used for experiment as seen in Fig. 6.
Using all points within the circle has a good success rate. However, in order to further reduce the workload of the method instead of using all points within a circle reduced points are used for experiment as seen in Fig. 6.
upper and bottom eyelids. If this distance is close to one or two pixels, then it can be assumed that the eye is shut. However, there is also a matter of deciding in which condition it should be deemed there is a blink. Should a moment where the eye is completely shut be chosen or should moments where eyes are partially shut also be added? As seen in Fig. 8, the act of blinking is composed of a few stages. When looking from the perspective of getting information out of images, in a state where eyes are partially shut and in a state where eyes are completely shut, they are the same because both have the same meaning that the eyes are in a state where they do not concentrate on looking. So, if the distance between the eyelids is smaller than half of the eye radius, it can be assumed that the eye is closed. Even though this is a practical approach there are still some negligible issues. When a person laughs or looks at a bright sight, people tend to partially close their eyes which may be interpreted as a blink. It is not easy to detect the difference between a blink and a laugh or a bright light reflex. It is possible to use other landmark points which correspond to mouth to detect the laugh or it may be possible to check the brightness by using a histogram. However, it would be too much effort to detect the difference, and it is an acceptable error. Also, it will not make much difference in practice.
upper and bottom eyelids. If this distance is close to one or two pixels, then it can be assumed that the eye is shut. However, there is also a matter of deciding in which condition it should be deemed there is a blink. Should a moment where the eye is completely shut be chosen or should moments where eyes are partially shut also be added? As seen in Fig. 8, the act of blinking is composed of a few stages. When looking from the perspective of getting information out of images, in a state where eyes are partially shut and in a state where eyes are completely shut, they are the same because both have the same meaning that the eyes are in a state where they do not concentrate on looking. So, if the distance between the eyelids is smaller than half of the eye radius, it can be assumed that the eye is closed. Even though this is a practical approach there are still some negligible issues. When a person laughs or looks at a bright sight, people tend to partially close their eyes which may be interpreted as a blink. It is not easy to detect the difference between a blink and a laugh or a bright light reflex. It is possible to use other landmark points which correspond to mouth to detect the laugh or it may be possible to check the brightness by using a histogram. However, it would be too much effort to detect the difference, and it is an acceptable error. Also, it will not make much difference in practice.
3.2 Evaluation
3.2 Evaluation
Figure 9 Example of the successful results for BiolD database (e < 0.025)
Figure 9 Example of the successful results for BiolD database (e < 0.025)
Figure 11 Example of the unsuccessful result of BiolD database (e < 0.025) Figure 10 Example of the unsuccessful result for TalkingFace database (e < 0.025)
Figure 11 Example of the unsuccessful result of BiolD database (e < 0.025) Figure 10 Example of the unsuccessful result for TalkingFace database (e < 0.025)
Figure 10 Example of the successful result for TalkingFace database (e < 0.025)
Figure 10 Example of the successful result for TalkingFace database (e < 0.025)
(e), (f), and (g) are the lists which have a low point count. In descending order, they are sorted (g), (e), and (f) according to the number of points they contain. The results of the (f) are surprisingly good for TalkingFace database, As seen in Fig. 6, (h) is the input list where all points within the eye circle are used. Thus the best result is expected from this list and as seen in Tab. 2 for both databases (h) has the best results. (b), (c), and (d) have the lowest score in both databases, and all of these input lists have the points from a circle which has a half radius of the eye. Usually, reflection occurs within this circle and, we believe, that is the reason for the low success rates.
(e), (f), and (g) are the lists which have a low point count. In descending order, they are sorted (g), (e), and (f) according to the number of points they contain. The results of the (f) are surprisingly good for TalkingFace database, As seen in Fig. 6, (h) is the input list where all points within the eye circle are used. Thus the best result is expected from this list and as seen in Tab. 2 for both databases (h) has the best results. (b), (c), and (d) have the lowest score in both databases, and all of these input lists have the points from a circle which has a half radius of the eye. Usually, reflection occurs within this circle and, we believe, that is the reason for the low success rates.
Figure 12 In some cases eye positions ns of the BiolD database are not accurate. These images are marked as unsuccessful for e < 0.05 In Fig. 13 and Fig. 14 unsuccessful results for BioID and TalkingFace databases are given. However, as seen in the images these results were successful. It has been noticed that some of the eye positions are not annotated accurately.
Figure 12 In some cases eye positions ns of the BiolD database are not accurate. These images are marked as unsuccessful for e < 0.05 In Fig. 13 and Fig. 14 unsuccessful results for BioID and TalkingFace databases are given. However, as seen in the images these results were successful. It has been noticed that some of the eye positions are not annotated accurately.
Figure 14 Example of the successful result of ColorFeret database (e < 0.025) Our attempt to correct the inaccurate eye positions showed that even the same person might mark different locations as an eye in different trials, which is why it has been decided to use given eye positions. However, it should be noted that for (e < 0.025) some of the successful results will be marked as unsuccessful.
Figure 14 Example of the successful result of ColorFeret database (e < 0.025) Our attempt to correct the inaccurate eye positions showed that even the same person might mark different locations as an eye in different trials, which is why it has been decided to use given eye positions. However, it should be noted that for (e < 0.025) some of the successful results will be marked as unsuccessful.
Figure 13 Successful localization, which is marked as unsuccessful for Talkinc Face database (e < 0.025)
Figure 13 Successful localization, which is marked as unsuccessful for Talkinc Face database (e < 0.025)
Figure 15 Example of the unsuccessful result of ColorFeret database (e< 0.025) out of the three databases. However, it has also the poorest eye position information. Usually, they merely did mark somewhere within an eye. They did not mark eye centers. In Fig. 15, some successful results from ColorFeret database are given. It can be seen that as the resolution grows the method can cope with it. Fig. 16 shows yet another example of failed eye corner detection for ColorFeret database. As seen in images, eye corners are not accurate which causes unsuccessful results. However, since eye positions are not accurate, ColorFeret database has not been used for success comparison.
Figure 15 Example of the unsuccessful result of ColorFeret database (e< 0.025) out of the three databases. However, it has also the poorest eye position information. Usually, they merely did mark somewhere within an eye. They did not mark eye centers. In Fig. 15, some successful results from ColorFeret database are given. It can be seen that as the resolution grows the method can cope with it. Fig. 16 shows yet another example of failed eye corner detection for ColorFeret database. As seen in images, eye corners are not accurate which causes unsuccessful results. However, since eye positions are not accurate, ColorFeret database has not been used for success comparison.
Table 3 Experimental Results For Two Stage Window Movements 3.3 Comparison with the State of the Art
Table 3 Experimental Results For Two Stage Window Movements 3.3 Comparison with the State of the Art
Easymatch method seems to be average against state of the art for the BioID database. However, it should be noted even though all of the methods use BioID database some of them use the database partially such as removing images with people who use eyeglasses or removing images with closed eyes. Also, some of the studies [6, 8, 10, 11] had used the Viola-Jones face detector to detect the face and according to our tests; successful face detection
Easymatch method seems to be average against state of the art for the BioID database. However, it should be noted even though all of the methods use BioID database some of them use the database partially such as removing images with people who use eyeglasses or removing images with closed eyes. Also, some of the studies [6, 8, 10, 11] had used the Viola-Jones face detector to detect the face and according to our tests; successful face detection
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