**5. Experimental results**

#### **5.1 Introduction**

With face recognition, a database usually exists that stores a group of human faces with known identities. In a testing image, once a face is detected, the face is cropped from the image or video as a probe to check with the database for possible matches. The matching algorithm produces a similarity measure for each of the comparing pairs.

Variations among images from the same face due to changes in illumination are typically larger than variations rose from a change of face identity. In an effort to address the illumination and camera variations, a database was created, considering these variations to evaluate the proposed techniques.

Besides the regular room lights, four additional spot lights are located in the front of the person that can be turned off and on in sequence to obtain face images under different illumination conditions. Note that it is important to capture visual and thermal images at the same time in order to see the variations in the facial images. Visual and thermal images are captured almost at the same time. Although radiometric calibration is important, the thermal camera can not be calibrated because of current IR camera characteristics.The Pinnacle (Pinnacle Systems Ltd.) software has been implemented to capture 16 visual and thermal images at the same time. Figure 6 (a) and (e) shows an example of visual and thermal images taken at the same time.

206 Bio-Inspired Computational Algorithms and Their Applications

(optimum weights for maximizing the entropy of *F*) for entropy and IS from which it can

Fig. 5. Fusion Results: (top-left) highest value from IR or Visual Images; (top-right) lowest value form IR or Visual Images; (bottom-left) average of IR and Visual Images; (bottom-

In this section, CGA based image fusion algorithm was introduced and compared with other classical PLIFs. The results show that CGA based image fusion gives better result than

With face recognition, a database usually exists that stores a group of human faces with known identities. In a testing image, once a face is detected, the face is cropped from the image or video as a probe to check with the database for possible matches. The matching

Variations among images from the same face due to changes in illumination are typically larger than variations rose from a change of face identity. In an effort to address the illumination and camera variations, a database was created, considering these variations to

Besides the regular room lights, four additional spot lights are located in the front of the person that can be turned off and on in sequence to obtain face images under different illumination conditions. Note that it is important to capture visual and thermal images at the same time in order to see the variations in the facial images. Visual and thermal images are captured almost at the same time. Although radiometric calibration is important, the thermal camera can not be calibrated because of current IR camera characteristics.The Pinnacle (Pinnacle Systems Ltd.) software has been implemented to capture 16 visual and thermal images at the same time. Figure 6 (a) and (e) shows an example of visual and

algorithm produces a similarity measure for each of the comparing pairs.

concluded that CGA performs better than other PLIFs.

right) threshold value.

**5. Experimental results** 

evaluate the proposed techniques.

thermal images taken at the same time.

**4.5 Conclusion** 

other PLIFs.

**5.1 Introduction** 


Table 2. Performance Comparision of Image Fusion Methods for Figure 4 and Figure 5.

In this chapter, the focus is on visual image enhancement. Then the visual images will be registered with the IR images based landmark registration algorithm. Finally, the registered IR and visual images are fused for face recognition.
