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

Table 2.3
Summary of performance of various face detection approaches.
Authors Year Head
Pose
Féraud et al.
[19]
Maio et al.
[20]
Schneiderman
et al. [22]
Garcia et al.
[21]
Rowley et al.
[24], [23]
Yow et al.
[26]
2001 Frontal
to
profile
Test
Databases
Sussex;
CMU test1;
Web images
Detection Rate
100% for Sussex;
81% ∼ 86% for CMU test1;
74.7% ∼ 80.1% for Web
images.
2000 Frontal Static images 89.53% ∼ 91.34%
2000 Frontal
to
profile
1999 Frontal
to near
frontal
1998 (Upright)
frontal
1997 Frontal
to
profile
CMU;
images
Web
MPEG videos 93.27%
CMU;
FERET;
Web images
75.24% ∼ 92.7%
CMU 84% ∼ 92%
Lew et al. [27] 1996 Frontal MIT; CMU;
Leiden
86%[24]; 79.6%[23] for rotated
faces
87% ∼ 95%
the appearance-based (holistic) approach has greatly advanced the field of face recognition.
By categorizing face detection methods based on their representations of the
face, we observe that detection/recognition algorithms using holistic representations
have the advantage of finding/identifying small faces or faces in poor-quality images
(i.e. detection/recognition under uncertainty), while those using geometrical facial
features provide a good solution for detecting/recognizing faces in different poses and
expressions. The internal representation of a human face substantially affects the performance
and design of a detection or recognition system. A seamless combination of
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