Facial Feature Localizer Essay

Facial Feature Localizer Essay

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Abstract. This paper describes a facial feature localizer framework that is capable of processing images rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new vector space for image representation. Due to this definition, an m×n image consists of m vectors in an n-dimensional space. The second is proving that an image consists of observations set from similar distribution. The third contribution is proposing a one-to-one linear transform called the “Linear Principal Transform”, which allows extracting all features of image rapidly and efficiently. This new transform reduces the dimension of the image from n to one. A set of experiments on the IFDB image data set is presented. The system yields eye feature extraction performance comparable to the best previous systems. Success rate of the proposed method is 95.2%.
Keywords: Facial Features Extraction; Image Space; Linear Principal Transform; Iris Detection; Q-Q Plot.
1 Introduction
Locating the exact face features – eye, nose, lips, chin– is a kind of exploiting low-dimensional structures in a face as a high-dimensional data. This is the most significant stage in applications like Face Recognition [1], Facial Expression [2], Face Detection [3], Animation [5], Age Classification [4], etc.
Many methods have proposed to solve the problem. These methods can be classified in four geometric based, color based, template based, and appearance based categories. The last two methods require use of an expert or a machine generated template(s). These templates are often based on learning of subspaces or sub-manifolds. Template matching [6], ASM [7], SVM [9], and AdaBoost [10] fall into this category. Although these method...


... middle of paper ...


...mits.

Fig. 7. A comparison between the eye detection results.
Table 1. Comparison of eye localization methods on the IFDB for different εmax error limits.


4 Conclusion
This paper describes a facial feature localizer that is capable of processing images rapidly while achieving high detection rates. The key contributions of this paper are introduction of an N-dimensional vector space for image, proving the corollary which states that the image is a set of observations from similar distribution and proposing a one-to-one linear transform that allows extracting all features of image quickly and efficiently. This new transform reduces the dimension of the image from N to one. Furthermore, this algorithm is free of the shortcomings of other algorithms such as failure in encountering nonstandard illumination, occlusion, high training time, low speed search, etc.

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