Face Matching
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▶ deformable models, which encodes the prior knowledge of face shape or appearance, to take into account the low-level image evidences and find the face that is present in the image.
Introduction
Face Aging Face aging is to predict the future appearance of human face by learning the aging patterns, child growth, and adult aging are two type of aging. ▶ And-Or Graph Model for Faces
Face Alignment L EON G U, TAKEO K ANADE Carnegie Mellon University, Pittsburgh, PA, USA
Synonyms Face registration; Face matching
Definition Face alignment is a computer vision technology for identifying the geometric structure of human faces in digital images. Given the location and size of a face, it automatically determines the shape of the face components such as eyes and nose. A face alignment program typically operates by iteratively adjusting a #
2009 Springer Science+Business Media, LLC
The ability of understanding and interpreting facial structures is important for many image analysis tasks. Suppose that, if we want to identify a person from a surveillance camera, a natural approach would be running the face image of the person through a database of known faces, examining the differences and identifying the best match. However, simply subtracting one image from another would not yield the desirable differences (as shown in Fig. 1), unless two faces are properly aligned. The goal of face alignment is to establish correspondence among different faces, so that the subsequent image analysis tasks can be performed on a common basis. The main challenge in face alignment arises from pervasive ambiguities in low-level image features. Consider the examples shown in Fig. 2. While the main face structures are present in the ▶ feature maps, the contours of face components are frequently disrupted by gaps or corrupted by spurious fragments. Strong gradient responses could be due to reflectance, occlusion, fine facial texture, or background clutter. In contrast, the boundaries of face components such as nose and eyebrow are often obscure and incomplete. Looking for face components separately is difficult and often yields noisy results. Rather than searching individual face components and expecting the face structure to emerge from the results, a better strategy is imposing the structure explicitly from the beginning. A majority of work in the field are developed based on this strategy. Deformable template [1], for example, is an elastic model which resembles face structure by assemblies of flexible curves. A set
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Face Alignment
Face Alignment. Figure 1 To compare two face images, by directly adding them or subtracting one from another does not produce the desired result. Face alignment enables to establish correspondences between different images, so that the subsequent tasks can be performed on a common basis.
Face Alignment. Figure 2 The major difficulty in face alignment is low-level image ambiguities. Face topologies could be significantly corrupted in the gradient feature maps (second row), due to various factors such as r
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