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1 Introduction
Biometrics focuses on simulate the human ability to associate one or a set of corporal features of a person in a unique way by uses a specific representation, this representation is knows as identity. The biometric identification has become a real solution for many social, corporate and commercial activities where it is necessary the verification of the identity [11,1,16].
In principle, any physiological feature can verify or recognize the biometric identity of a person, these features can be group in several modalities as hands, faces, behavior or medical-chemistry features [17].
Visible spectrum face recognition is the identification way more natural which has a higher universality, collectability and acceptability front to the other biometrics modalities, but is weak in the invariance and distinctiveness criterion to be a good biometric [2,11].
Although face recognition has more of two decades, many of the research has been over visible spectrum [2,15,17]. This approach has difficulties when there are lighting variability, chance of pose or different facial expressions.
In order to improve the face recognition, the infrared spectrum arises as a good representation to solve this drawbacks in biometric identification [6,12,8,15].
This paper is written with the intention to apply the method of higher intensity region of thermogram of Buddaraju over the propose of Wu and test and contrast the performance of this modification with the original propose of Buddharaju [3]. We prove that the perfusion regions with higher intensity are equivalent at the zones of the Buddaraju propose. Our method combines, in first place, the works of Buddaraju and Wu and one simple region shape descriptor is applied to form a nine-dimensional vector feature.
After, it is used the same method over the Buddharaju propose and the performance of both proposes are compared. The UCH image database was employed for this study which has outdoor, indoor and face angle rotations conditions. In order to highlight the efficiency of the proposed face recognition approach, several classification algorithms were used in our study and we proved that both proposes hold enough information to biometric face representation because of the difference in performance accuracy are not significance. However, the Wu thermogram add more computational work.
2 Related Work
Several approaches and techniques have been proposed for a quantitative description. One of the first work was made by Yoshitomiet al. They shown that the infrared spectrum is more robust with the lighting variability problem of the visible [22]. After, Friedrich et al., proposed a method based on eigenfaces as face descriptor of thermograms and found that these kinds of images were less affected by the change of pose or facial expressions [7]. The following works have been focused on different ways to describe the thermograms and the frequency domain is one approach widely used [9,6]. By another hand, an approach derived is the getting of information physiologic contained inside infrared images.
For instance, Prokoski et al., found that it is possible the detection of the face vascular net in thermograms which could be used as a biometric descriptor [13]. Buddharaju et al., used this idea and proposed a process by which the vascular net is detected. Their hypothesis is based on the existence of zones with high values of intensity in the pixels of the thermogram and that this values they are correlated with the presence of some blood vessel. The method finds the regions with the higher thermogram intensities and they are isolated by morphological transforms. These regions are thinning and its skeleton is formed. In this skeleton are marked the breaching points, called Thermal Minutia Points (TMP). They are used as minutiae points [4,5,3].
The work was criticized because the skeleton did not necessarily show blood vessels and it did not take into account the physics and physiological conditions of the heat transfer [19,18,21].
Wu takes into account the heat transfer condition in thermograms and suggests changes the name of the regions detected from the vascular net to the perfusion zones. This approach modifies the pixels values of the thermogram and generates a new image which is more robust to temperature changes in which it is taked. Wu and Zhang tested this representation using cosine transform as descriptor and found that can be used as a good biometric [20].
3 Face Recognition Method
In this section, it is described the methodology following for faces recognition based on simple shape descriptor over maximal perfusion zones. Figure 1 shows the methodological architecture.
Step 1: Thermogram transformation into an image of blood perfusion. This step is made by the propose of Wu, where each intensity pixel is modified by (1):
where
T is the thermal intensity pixel value, Ta is the artery temperature and Te is the ambient temperature.
Step 2: It is applied the method of the highest intensity blood vessels extraction proposed by Buddharaju: The anisotropic filter was set up under Matlab platform with a gradient modulus threshold value equal to 60, an integration constant equal to 1/5 and the option 1. After, it was implemented a Tophat morphological transform with a disk as structure element of radius equal to 3. Finally, it was used a closed morphological operation for clear the image with a disk with a radius equal to two.
Step 3: The image is segmented in 9 equal dimensional regions, as it is shown in Figure 1.
Step 4: For each sub-image a simple shape descriptor is extracted, in our case it is use the normalize E factor (NEF) [14]. The NEF uses the internal or external border information to give us a measure of the shape porosity. In our approach, each image is taken as an object with holes. After, a nine feature vector is formed with the NEF values. The example is shown in Figure 1.
Step 5: Finally, the process end with the classification of the database using the NEF description by the following classifiers: Support Vector Machine, K-Nearest Neighbor (K = 3), Parzen, Normal densities based linear (multi-class), Nearest mean linear, Normal densities based quadratic (multi-class), Minimum least square linear, Logistic Linear, Quadratic, Naive Bayes, LC based on PCA expansion on the joint data.
4 Results
For testing the performance of perfusion zones as biometric identification, we used the UCH Thermal data base [10]. This database is ideal because the thermograms are taken in unconstrained environments, with face rotation, indoor, outdoor session and facial expressions. The images used for these tests were images had a spatial size of 81×150 pixel. To measure the effectiveness of this representation, a 4-folds statistical test done by 5 iterations was implemented in the PrTools library under Matlab platform. Table 1 shows the performance by the average recognition rate which is given for each classification algorithm when it is applied our method indoor and rotation conditions. The best overall performance was for a support vector machine and the worse result was for a normal densities based linear algorithm. A standard deviation (SD) got join to the time process for each algorithm.
Table 1 Performance of higher perfusions zones on UCH image database using our propose method
| Algorithm | Accuracy | SD | Speed |
|---|---|---|---|
| SVM | 89.49% | 0.0069 | 623.162712 s |
| KNN | 61.62% | 0.0138 | 2.793641 s |
| Parzen | 65.80% | 0.0081 | 2.050866 s |
| NDBL | 48.60% | 0.0039 | 7.254782 s |
| NML | 63.26% | 0.0081 | 4.507564 s |
| NDBQ | 75.77% | 0.01 | 3.559874 s |
| MLQL | 59.51% | 0.0069 | 31.038115 s |
| LL | 57.84% | 0.0677 | 34.936810 s |
| Quadratic | 78.68% | 0.02 | 41.189440 s |
| NB | 68.03% | 0.0107 | 1.234846 s |
| LC | 47.71% | 0.0081 | 13.115993 s |
With the aim to found some difference between the original propose of Buddharaju and the Wu modification, we tested the same extracted features approach without uses heat transfer equation. The results are described in Table 2. In this case, the support vector machine again got the best classification percentage. However, the difference between both approach are not significative. There are not an important difference with the classification values of the others algorithms.
Table 2 Performance of classification using NEF descriptor on UCH image database when applying only Buddharaju approach
| Algorithm | Accuracy | SD | Speed |
|---|---|---|---|
| SVM | 89.22% | 0.0034 | 639.940778 s |
| KNN | 60.51% | 0.0062 | 2.979696 s |
| Parzen | 63.13% | 0.0056 | 2.159387 s |
| NDBL | 50.00% | 0.0076 | 7.734073 s |
| NML | 62.61% | 0.0088 | 4.526179 s |
| NDBQ | 74.72% | 0.0126 | 4.040726 s |
| MLQL | 58.06% | 0.0065 | 32.249418 s |
| LL | 54.58% | 0.0088 | 34.873280 s |
| Quadratic | 77.57% | 0.0109 | 41.106188 s |
| NB | 67.84% | 0.0102 | 1.387489 s |
| LC | 49.84% | 0.0018 | 13.063554 s |
Finally, we applied our method separately to the Buddharaju and Wu approaches, with the aim to test if there are difference in outdoor condition, using our face descriptor. The results are illustrated in the Table 3, where only the best classification percentage is showed. We could hope that the Wu propose was better than Buddharaju approach. However, we did not find a significative difference in classification rate.
5 Conclusion and Future Work
In this paper, we analyzed how the zones of highest intensity in a modified thermogram can be used as biometric descriptor in combination with the vane net detection method proposed by Buddharaju. For showing this property, a method was designed by partition the vane net image in nine sub-images. Our propose showed a robust behavior to change of temperature, environment, face rotation and physiological condition, properties of the well-know UCH Thermal Face data image base. This robustness is evident when a simple shape descriptor is used to conform a characteristic vector and good percentages in classification accuracy is obtained. Unlike the work of Hermosilla et al. [10], where a study of different description algorithms are tested using the totality of the image, we concentrated the work in lower the computational cost in time and spaces by a reduce representation of the thermogram. The result showed that the representation can be uniquely associated. However, it can observed that the modification make by Wu is not had a great impact when the zones of highest blood intensity are extracted and it is used our approach of description. The limitation of our approach is the kind of descriptor, which is weak in affine transformation and can be more sensitive by the image face angle. However, the work is concerned with the representation, not with the its description. The future work is concerned with applied more complexes descriptor and description methods.
