A rough fuzzy neural network approach for robust face detection and tracking - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 251

Table 1
Experimental Results on CMU-PIE, Color FERET, and IMM Databases

Database Detection Rate (%) False Alarms

CMU-PIE 92.16 175

color FERET 96.53 16

IMM 98.65 0

Table 1 shows that the proposed algorithm for face-detection exhibits very good perform-
ance in detecting faces, which is also affected by scene condition variations, such as the pres-
ence of a complex background and uncontrolled illumination. However, the MS-RNN method
is noted to be sensitive to the images where the background (neutral illumination) is turned
of, mainly those obtained from CMU-PIE database.

7.1.2 Experiment 2
This experiment used the CalTech [ 27 ] face database. A total of 450 frontal images of 27 people
of different races and facial expressions were included. Each image was of 896 × 592 pixels.
The experiment employed detection and false alarms to evaluate the face-detection perform-
ance. Figure 12 shows some detection results.

FIGURE 12 Face-detection results by the proposed method in Experiment 2.

Table 2 shows the comparison of the results of the MS-RNN face-detection method on
CalTech databases with other fast face-detection methods reported in Refs. [ 6 , 9 ], which may be
applied to real-time face tracking. Specifically, the first method used for comparison extends
the detection in gray-images method presented in Ref. [ 6 ] to detection in color images. The
second method segments skin colors, in the HSV color space, using a self-organizing Takagi-
Sugeno fuzzy network with support vector learning [ 9 ]. A fuzzy system is used to eliminate
the effects of illumination, to adaptively determine the fuzzy classifier segmentation threshold
according to the illumination of an image. The proposed method showed the highest detection
rate as well as the smallest number of false alarms, when compared with other methods.

Table 2
Experimental Results on CalTech Database

Method Detection Rate (%) False Alarms

Texture + SVM [ 6 ] 95.7 91

SOTFN-SV + IFAT [ 9 ] 95.7 67

Proposed method 98.43 20