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

1.9 Facial features/components: (a) five kinds of facial features (i.e., eyebrows,
eyes, nose, ears, and mouth) in a face for reading faces in physiognomy
(downloaded from [11]); (b) a frontal semantic face graph, whose nodes
are facial components that are filled with different shades. . . . . . . 11
1.10 Similarity of frontal faces between (a) twins (downloaded from [12]); and
(b) a father and his son (downloaded from [13]). . . . . . . . . . . . . 13
1.11 System diagram of our 3D model-based face recognition system using registered
range and color images. . . . . . . . . . . . . . . . . . . . . . 16
1.12 System diagram of our 3D model-based face recognition system without
the use of range data. . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.13 Face images taken under unconstrained environments: (a) a crowd of people
(downloaded from [14]); (b) a photo taken at a swimming pool. . 18
1.14 Face images for our detection algorithm: (a) a montage image containing
images adapted from MPEG7 content set [15]; (b) a family photo. . . 20
1.15 Face images not suitable for our detection algorithm: (a) cropped image
(downloaded from [16]); (b) a performer wearing make-up (from [14]);
(c) people wearing face masks (from [14]). . . . . . . . . . . . . . . . 20
1.16 Graphical user interfaces of the FaceGen modeller [17]. A 3D face model
shown (a) with texture mapping; (b) with wireframe overlaid. . . . . 22
1.17 A face retrieval interface of the FACEit system [18]: the system gives the
most similar face in a database given a query face image. . . . . . . . 27
2.1 Outputs of several face detection algorithms; (a), (b) Féraud et al. [19];
(c) Maio et al. [20]; (d) Garcia et al. [21]; (e), (f) Schneiderman et al.
[22]; (g) Rowley et al. [23]; (h), (i) Rowley et al. [24]; (j) Sung et al.
[25]; (k) Yow et al. [26]; (l) Lew et al. [27]. . . . . . . . . . . . . . . . 36
2.1 (Cont’d). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.2 Examples of face images are selected from (a) the FERET database [28];
(b) the MIT database [29]; (c) the XM2VTS database [30]. . . . . . . 41
2.3 Internal representations of the PCA-based approach and the LDA-based
approach (from Weng and Swets [31]). The average (mean) images are
shown in the first column. Most Expressive Features (MEF) and Most
Discriminating Features (MDF) are shown in (a) and (b), respectively. 42
2.4 Internal representations of the EBGM-based approach (from Wiskott et
al. [32]): (a) a graph is overlaid on a face image; (b) a reconstruction
of the image from the graph; (c) a reconstruction of the image from a
face bunch graph using the best fitting jet at each node. Images are
downloaded from [33]; (d) a bunch graph whose nodes are associated
with a bunch of jets [33]; (e) an alternative interpretation of the concept
of a bunch graph [33]. . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.5 Internal representations of the LFA-based approach (from Penev and Atick
[34]). (a) An average face image is marked with five localized features;
(b) five topographic kernels associated with the five localized features
are shown in the top row, and the corresponding residual correlations
are shown in the bottom row. . . . . . . . . . . . . . . . . . . . . . . 44
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