Advances in Face Image Analysis: Techniques and Technologies by Yu-Jin Zhang

By Yu-Jin Zhang

Video and photograph research of the human face presents a wealth of knowledge in regards to the person, , together with age, habit, healthiness and career. With study consistently being performed into a number of purposes of this box, a complete and exact quantity of the recent developments of face photograph research is fashionable. Advances in face photo research: suggestions and applied sciences fulfills this desire, reviewing and surveying new forward-thinking examine and improvement in face snapshot research applied sciences. With greater than 30 top specialists from all over the world offering complete assurance of varied branches of face photograph research, this booklet is a important asset for college kids, researchers and practitioners engaged within the examine, examine and improvement of face photograph research innovations.

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LITERATURE ABOUT AUTOMATIC FACE RECOGNITION Face Recognition can be considered as a special example of pattern recognition, in particular as a template matching problem. Such problem is hard, as it is intrinsically non linear and must be addressed within a high-dimensional space. The following subsections will summarize the most widely investigated approaches for 2D face recognition. The aim is to present general issues, 17 Face Searching in Large Databases Table 1. Most important face databases.

For example, the innovation in (Zhao, 2008) with respect to other literature methods consists in extracting a projection matrix in a full space instead of in a subspace. PCA, LDA and Bayesian analysis are unified under the same framework in (Wang, & Tang, 2004), after modelling face difference with three components: intrinsic difference, transformation difference, and noise. The inherent relationship among different subspace methods is exploited to extract discriminating information. Better recognition performance is achieved than standard subspace methods.

2001) UPCA Unified PCA (Shan et al. 2008) 7 Face, Image, and Analysis has the most similar pose and lighting conditions. View-synthesis methods generate synthetic views from the input probe image that have similar pose and lighting conditions as the gallery images in order to improve the matching performance. The desired views can be synthesized by learning the mapping function between pairs of training images or by combining with some of the above 3-D face models. Video can be utilized in conjunction with 3-D face models in face image analysis.

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