Fusion of Visual and Thermal Face Recognition Techniques: A ...

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(a) (b) (c) Figure 2: Example of visual and thermal images from Equinox. (a) Visual images, (b) original thermal images, and (c) equalized thermal images 2.2 Image Acquisition Setup Variations between the images of the same face due to changes in illumination and viewing directions are typically larger than image variations raised from a change in face identity. Other factors such as facial expressions and pose variations further complicate the face recognition task. Simple disguises such as a fake nose or beard substantially change a person’s visual appearance. In an effort to solve main factors that degrade the performance of visual face recognition systems such as illumination, expression, rotation, and disguise, we have created our own database considering these variations. In order to provide a comparatively large image gallery for the face recognition project, we consider changing illumination conditions and various expressions. Besides the regular room 17
lights, we have two extra spot lights locate in the front of the person that we can turn off and on in sequence to obtain face images under different illumination conditions. The concept here is to perform 180 degree scanning of the human face and then record the process. As this is a 1D scanning process and only scanning degrees are of concern, we put a rotating table to use. An extended shaft is mounted on a rotating table and the cameras are fixed at the end of the shaft. Figure 3 shows the rotary system platform and its dimensions. During scanning, the motor is controlled in rotation from a host computer and motion is conveyed through a central shaft to the rotating bar. When a person is seated directly beneath the shaft, the cameras start rotating and real time video data are sent to a video capturing computer through RCA ports. Two frame grabbers, which are connected to two individual cameras, transfer the video to image frames. After the 180-degree scanning (side to side), multiple views of human face data are acquired. It is important to capture visual and thermal images at the same time in order to see the variations in the facial images. While a rotary system rotates with a constant speed, visual and thermal images are captured almost at the same time. The frame grabber from Matrox can achieve 30 frames per second. Due to the large size of the images, we limit capture to 5 framers per second with both images having 320*240 image resolution. There is a slight delay (1/30 sec) after capturing one modality; however, there will be no difference in the facial images. Although radiometric calibration is important, we cannot calibrate the thermal camera because of current Raytheon IR camera characteristics and lack of resources such as blackbody source. Figure 3 shows the devices used d to capture both visual and uncalibrated thermal images. As shown Figure 3 (a), and (b), visual and thermal cameras are attached in the middle of the bar in the rotary system. Two fixed 18
Page 1 and 2: Fusion of Visual and Thermal Face R
Page 3 and 4: Abstract This paper describes how f
Page 5 and 6: List of Tables Table 1: Datasets...
Page 7 and 8: Figure 27: Example images of same i
Page 9 and 10: heat emitted by objects. The use of
Page 11 and 12: illumination source can reduce ligh
Page 13 and 14: identification recognition systems
Page 15 and 16: The LFA constructs a family of loca
Page 17 and 18: interest in high-end security appli
Page 19 and 20: covers any area, which deals with u
Page 21 and 22: decision fusion schemes (average co
Page 23: sensors for experimentation and sta
Page 27 and 28: taken with the eyeglasses off. For
Page 29 and 30: (a) (b) (c) Figure 6: A data fusion
Page 31 and 32: Edge detection methods such as Cann
Page 33 and 34: (a) (b) (c) Figure 9: Average therm
Page 35 and 36: 2 2 F(A,T)= AT = ax + bxy + cy + dx
Page 37 and 38: ignored to achieve better performan
Page 39 and 40: detection algorithm as described ab
Page 41 and 42: Eyeglasses (b) No eyeglasses (c) Fi
Page 43 and 44: Table 3: Performance for eyeglass d
Page 45 and 46: 4.1 Feature Based Face Recognition
Page 47 and 48: In addition to feature based method
Page 49 and 50: Figure 24: Accuracy results based o
Page 51 and 52: other factors such as compression a
Page 53 and 54: Figure 29: Pose test summary Table
Page 55 and 56: ‘Medium’ as not ambient but sti
Page 57 and 58: Based on the identification experim
Page 59 and 60: Figure 33: Decision fusion example
Page 61 and 62: in performance in this case. This i
Page 63 and 64: Figure 36: Performance comparison w
Page 65 and 66: Figure 39: Performance comparison o
Page 67 and 68: average eye template in thermal ima
Page 69 and 70: [14] P. S. Penev and J. J. Atick,
Page 71 and 72: [30] P. J. Phillips, “Support Vec
Page 73 and 74: [47] http://www.equinoxsensors.com/
Page 75:
Vita Jingu Heo was born in Daegu, K
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