Keywords: Facial Features Extraction; Image Space; Linear Principal Transform; Iris Detection; Q-Q Plot.
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 , Facial Expression , Face Detection , Animation , Age Classification , 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 , ASM , SVM , and AdaBoost  fall into this category. Although these method...
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Fig. 7. A comparison between the eye detection results.
Table 1. Comparison of eye localization methods on the IFDB for different εmax error limits.
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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- 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.... [tags: Image Representation]
1989 words (5.7 pages)
- Facial feature extraction is one of the most important challenges in the area of facial image processing. This step is the first in applications like Face Recognition , Facial Expression Recognition , Face Detection , Gender Classification , Age Classification , Animation  etc. Facial feature extraction, in general, refers to the detection of eyes, mouth, nose and other important facial components. Various techniques have been proposed in the literatures for this purpose and can be mainly classified in four groups: geometric feature-based, template-based, color segmentation-based and appearance-based approaches.... [tags: Facial recognicion, computers, ]
1016 words (2.9 pages)
- Abstract This paper is a note on a geometric-based facial feature localization method, which is proposed by Dehshibi and Bastanfard called Combined Projection Function (CPF). 1. Introduction This work sorts out the problem of facial feature localization featuring Linear Principal Transform (LPT). Facial feature extraction, in general, refers to detection of the eyes, mouth, nose and other important facial components. It is the most significant, preliminary stage in developing applications like Face Recognition , Facial Expression , Face Detection , Age Classification , Gender Classification , etc.... [tags: Research Analysis]
3894 words (11.1 pages)
- The ratios between facial features points are the third type of features, which their effects on family likeness are evaluated. These 9 ratios are calculated from the distances between facial feature points. In order to eliminate the dependency of the proposed algorithm to image scale, this set of ratios is utilized instead of distances between the facial feature points. These ratios are as follows: Eleven distances use to calculate the above ratios are illustrated in Figure 6. Locating the exact coordinates of the facial features points are crucial for calculating the ratios.... [tags: Research Analysis]
987 words (2.8 pages)
- 1. How are adults with baby-faced facial features perceived and treated differently than adults with mature facial features. What are two explanations for these effects. Mature adult faces are said to have features like small foreheads, angular chins, wrinkled skin, and smaller eyes (Kassin et al, 2010). On the contrary, baby-faced features include larger foreheads and eyes, rounded chins and cheeks, as well as smooth skin. We are naturally programmed to recognize these features. These recognitions pose the purpose of our natural instincts for us to identify infants and help to nurture them.... [tags: bias, judgement, stereotypes]
1285 words (3.7 pages)
- . Do you think people express themselves through their facial hair. Picture you walking down a busy downtown street, admiring the well-dressed people. When one of the coolest things came into view. A gentleman with a Van Dyke, which is a type of goatee, appeared in the view. That’s when a fascination with facial hair begun. There are many types, shapes, sizes and also colors of facial hair styles. With that being said, people do express themselves through their facial hair. Should jobs make new employees shave or even trim facial hair.... [tags: Beard, Facial hair, Shaving, Moustache]
770 words (2.2 pages)
- Concepts of Feature Computer Science In the 21s century, the field of computer science is experiencing fundamental transformations. In this article “Three New Concepts of Future Computer Science” was written as an observation result of a new computer science resulted from the six-year Sino-USA computer science leadership exchanges. The authors of this article , Zhi-Wei and Dan-Dan Tun, list three new concepts of the future computer science: computational lens, computational thinking, and ternary computing.... [tags: Computer science, Computer, Fields of science]
1035 words (3 pages)
- Amongst literature there have been many debates about whether there is a hemispheric preference for facial recognition. There is a general agreement that facial recognition is performed better and preferred on the right hemisphere rather than the left (Turkewitz & Ross-Kossak, 1984). However there have been studies suggesting that despite the right side being more preferred for facial recognition, depending on the nature of the facial stimuli, it has been demonstrated that both the left and right hemisphere can be preferred (Turkewitz & Ross-Kossak, 1984).... [tags: Cognition, Educational psychology, Clockwise, Face]
1320 words (3.8 pages)
- Amongst literature, there have been many debates about whether there is a hemispheric preference for facial recognition. There is a general agreement that facial recognition is performed better and preferred on the right hemisphere rather than the left (Turkewitz & Ross-Kossak, 1984). However there have been studies suggesting that despite the right side being more preferred for facial recognition, depending on the nature of the facial stimuli, it has been demonstrated that both the left and right hemisphere can be preferred (Turkewitz & Ross-Kossak, 1984).... [tags: Cognition, Educational psychology, Clockwise, Face]
1320 words (3.8 pages)
- Abstract Various applications have been proposed in the field of facial image processing throughout the last forty years or so, among which are face recognition, face detection, gender/age classification, facial expression, etc. However, no application related to the identification of similarity between family members has been introduced yet. This paper is the first experience, which considers this phenomenon and proposes a framework for clustering similar family members. Three features include: “The Whole Face,” “The Facial Feature Perimeters” and “The Ratio between Facial Features” have been used.... [tags: Medical Science]
1188 words (3.4 pages)