10. Face isothermal regions for drunk person identification

Drunk persons can be discriminated from sober ones using face isothermal regions. For this purpose, the morphological feature vector called pattern spectrum and support vector machines (SVMs) are employed for feature extraction and classification. Two different approaches are employed for extracting the isothermal regions of the face giving continuous vectors for intoxication identification. In the first one, the histogram of the face is divided (both of the sober and the drunk person) into equal regions. In the second approach, we examine in which isothermal region the whole forehead lies for the sober and the drunk person and which other regions of the face are within these isotherms.

Anisotropic diffusion was applied on the thermal infrared images for smoothing boundaries before extracting the isothermal regions. Specifically, anisotropic diffusion [28, 34] is used for noise removal, homogenization of regions, and detail preservation. The morphological feature vector called pattern spectrum [25, 26, 35] is extracted from the isothermal regions and transferred to the SVMs [36–38] for recognition of intoxicated persons. The identification success is found to be over 80% which is considered satisfactory.

Initially, four different types of isotherms were implemented, as follows:


The temperature difference between the sclera and the iris was examined [4, 6] based on the statistics of the pixels in these two regions by means of two different estimation procedures which correspond to two different discrimination features. In the first procedure, the ratio of the mean value of the pixels inside the sclera to the mean value of the pixels inside the iris was calculated. This procedure was performed on the left eye of each participant, both in the case he is sober and when he consumed alcohol. Consequently, two ratios of the mean value of the sclera to the mean value of the iris are available. It is observed that the ratio of the mean pixel value on the sclera to the mean value of the iris increases when the person has consumed alcohol. Specifically, for the 36 from the 41 cases, the specific ratio increases with alcohol consumption while only in 2 cases it decreases, and in the rest 3 it remains almost the same. The results were analyzed using the Students-t test, in order to support statistically the drunk screening capabilities of the proposed method from eye thermal images. In the second procedure, is estimated the variance of the pixels contained in the whole eye. This evaluation was performed for the left eye of each participant when the person is sober and when he is drunk. Therefore, two variances for each participant have been calculated, corresponding to sober and drunk person, respectively. It is observed that the variance increases in case that the person has consumed alcohol. Specifically, among the 41 participants only 4 presented decreased variance

Figure 15. The left thermal image corresponds to the face of the sober person while the right to the face of the

corresponding drunk person. For the drunk person the sclera becomes hotter from the iris.

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The proposed method presents the advantage that there is no need for comparison with the image of the sober person to infer for the intoxication situation. Simply, if an inspected person presents a gray level difference between the sclera and the iris, it has to be further tested for

Drunk persons can be discriminated from sober ones using face isothermal regions. For this purpose, the morphological feature vector called pattern spectrum and support vector machines

in the region of the eye for the drunk person compared to the sober one.

10. Face isothermal regions for drunk person identification

alcohol consumption with conventional means.

• Isolation of a single isotherm on the image.

Arbitrary determination of each isotherm is min and max (e.g. based on the minima of the histogram).

Figure 16 illustrated one example for each case. Experimentation on these four different types of isotherms revealed that only two of them are suitable for identifying drunk persons. Specifically, the first and the fourth type of isotherms, that is, equidistant and arbitrary determination as stated in the begging of the section. Using these two different types of isotherms in combination with anisotropic diffusion and morphology, isothermal features have been extracted for identifying intoxicated persons.

In the first approach, the histogram of the face, both of the sober and the drunk person, is divided into equal regions. It was found that the best number of isothermal regions is eight. In this case, we have the maximum perceptual information on the different isothermal regions. The majority of the pixels on the face belong to the two higher regions with pixel values from 191.25 up to 255. These two regions (191–223.125 and 223.125–255) occupy almost the whole face and their shape will be used to discriminate between sober and drunk. The shape also of the whole isothermal region 191.25–255 will be tested for identifying intoxicated persons. In Figure 17(a) and (b), the regions 191.25–223.125, 223.125–255 and the whole region 191.25–255, respectively, are shown in red. The regions become larger in case of the drunk person as it is easily recognizable. Accordingly, someone can decide which person is the drunk, if both red images are available. The basic problem is that the images of the sober person are never available and thus in real problems, there is not any capability of comparisons.

In the second approach, features that could help to recognize the drunk person without using the images of the sober counterpart, were tested. Accordingly, it is examined in which of the isotherms the forehead lies for the sober person and which other regions of the face are within

Figure 16. The four different types of isotherms on the face, (a) equidistant in the histogram range (0–255) in eight regions, (b) equal populated in the histogram range in eight regions, (c) isolation of a single isotherm on the image, (d) arbitrary determination of each isotherm min and max (e.g. based on the minima of the histogram).

these isotherms. After that it is examined in which isotherms the forehead lies for the drunk person and which other regions of the face are within these isotherms. Thus, we do not care about the value of each isotherm but we care about the isothermal regions of the face that include the forehead. From Figure 18, it is evident that some regions below the forehead are not isothermal with the forehead for the drunk person. The area of the red region decreases. This can be easily monitored in real time problems by an operator (e.g. policemen).

Measuring the decrease of area details is a task that can be performed using morphological granulometria [26, 35], that is, successive openings with an increase in size structuring element (pattern spectrum). All the above procedures for feature extraction using isothermal regions of the face were applied on the infrared images after a light smoothing preprocessing was performed. The smoothing processing which was applied is the anisotropic diffusion. The simple spectrum without diffusion gave the largest success which reaches 86%. These values correspond to Linear and Precomputed Kernel types in the SVMs. This case is the most interesting one, since the drunk person is recognized from the fact that the isothermal region in which the forehead

belongs contains actually no other region of the face. Thus, for intoxication identification, the thermal image of the drunk person is adequate to verify the drunkenness, and no comparison

Figure 18. (a) The region of isotherms that contains the forehead, for the sober person includes other regions as well. For

Figure 17. The images on the top row (a) correspond to sober person while in the bottom row to the corresponding drunk one. In the left column the red pixels have values in the range 191.25–223.125, in the middle column the pixel values lie in the range 223.125–255, while in the right column in the range 191.25–255. The regions, as it is easily recognizable, become

Intoxication Identification Using Thermal Imaging http://dx.doi.org/10.5772/intechopen.72128 167

with the image of the sober person is needed.

the drunk person (b) the forehead is more isolated.

wider in case of the drunk person.

Figure 17. The images on the top row (a) correspond to sober person while in the bottom row to the corresponding drunk one. In the left column the red pixels have values in the range 191.25–223.125, in the middle column the pixel values lie in the range 223.125–255, while in the right column in the range 191.25–255. The regions, as it is easily recognizable, become wider in case of the drunk person.

these isotherms. After that it is examined in which isotherms the forehead lies for the drunk person and which other regions of the face are within these isotherms. Thus, we do not care about the value of each isotherm but we care about the isothermal regions of the face that include the forehead. From Figure 18, it is evident that some regions below the forehead are not isothermal with the forehead for the drunk person. The area of the red region decreases.

Figure 16. The four different types of isotherms on the face, (a) equidistant in the histogram range (0–255) in eight regions, (b) equal populated in the histogram range in eight regions, (c) isolation of a single isotherm on the image, (d)

Measuring the decrease of area details is a task that can be performed using morphological granulometria [26, 35], that is, successive openings with an increase in size structuring element (pattern spectrum). All the above procedures for feature extraction using isothermal regions of the face were applied on the infrared images after a light smoothing preprocessing was performed. The smoothing processing which was applied is the anisotropic diffusion. The simple spectrum without diffusion gave the largest success which reaches 86%. These values correspond to Linear and Precomputed Kernel types in the SVMs. This case is the most interesting one, since the drunk person is recognized from the fact that the isothermal region in which the forehead

This can be easily monitored in real time problems by an operator (e.g. policemen).

arbitrary determination of each isotherm min and max (e.g. based on the minima of the histogram).

166 Human-Robot Interaction - Theory and Application

Figure 18. (a) The region of isotherms that contains the forehead, for the sober person includes other regions as well. For the drunk person (b) the forehead is more isolated.

belongs contains actually no other region of the face. Thus, for intoxication identification, the thermal image of the drunk person is adequate to verify the drunkenness, and no comparison with the image of the sober person is needed.
