Face Detection and Modeling for Recognition - Biometrics Research ...

(a) (b) (c) (d) (e)
Figure 3.18. Face detection results for subjects with facial hair. Each example
contains an original image (top) and a lighting-compensated image (bottom) overlaid
with face detection results.
the mouth, and the ellipse bounding a human face are found with a small amount
of tolerance, otherwise it is called a false positive. The detection rate is computed
by the ratio of the number of correct detections in a gallery to that of all human
faces in the gallery. Figure 3.20(a) shows a subset of the HHI images. The detection
results of our algorithm are shown in three stages. In the first stage, we show
the skin-tone regions (Fig. 3.20(b)) using pseudo-color; different colors correspond
to different skin-tone groups. In the second stage, we fuse bounding rectangles that
have significant overlapping areas with neighboring rectangles (Fig. 3.20(c)). Each
bounding rectangle indicates a face candidate. In the third stage, we locally detect
facial features for each face candidate. Figure 3.20(d) shows the final detection results
after these three stages. The detected faces are depicted by yellow-blue ellipses,
and the detected facial features (eyes and mouth) are connected by a triangle. The
detection rates and the number of false positives for different poses are summarized
in Table 3.1. The detection rate after the first two stages is about 97% for all poses.
After the third stage, the detection rate decreases to 89.40% for frontal faces, and to
85