By Yu-Jin Zhang
Video and photo research of the human face presents a wealth of data in regards to the person, , together with age, habit, wellbeing and fitness and occupation. With examine constantly being performed into a number of functions of this box, a finished and targeted quantity of the hot developments of face photograph research is widespread. Advances in face snapshot research: concepts and applied sciences fulfills this want, reviewing and surveying new forward-thinking learn and improvement in face photograph research applied sciences. With greater than 30 best specialists from around the globe supplying finished assurance of assorted branches of face photo research, this ebook is a precious asset for college students, researchers and practitioners engaged within the research, examine and improvement of face snapshot research thoughts.
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Additional resources for Advances in Face Image Analysis: Techniques and Technologies
Example text
Table 3 summarizes the characteristics of a number of methods. 3D FACE RECOGNITION In a recent work, Xu et al. (Xu, 2004) demonstrated that depth maps a more robust face representation, less affected by illumination changes, than intensity images, since a 3D model, either range data or polygonal mesh, retains a complete information about the face geometry. In a 3D face model, facial features are represented by local and global curvatures that can be considered as the real signature which identifies persons.
In the testing phase the pose of the subject is esteemed, and the identity with the nearest convex cone is assigned, using the Euclidean distance. This method is better than many others in terms of recognition rate. The reverse of the medal is the non-trivial computational cost of the training phase. As for robustness to lighting variations in purely 2D methods, the present trend is to exploit some image normalization technique, rather than devising distortion invariant recognition techniques. A comparison is performed in (Ruiz-del-Solar, & Quinteros, 2008) among many lighting normalization algorithms.
To guide any serious effort towards solving the problem in face image analysis, one needs to define detailed specifications of what the problem is and what would constitute a solution. Thus, the that incremental progress in research can be precisely quantified and different approaches can be compared through a standard procedure. The development of a robust algorithm for face image analysis, which is capable of functioning in unconstrained, real-world environments, will have far-reaching applications in our modern digital world.
