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 [1], Facial Expression Recognition [2], Face Detection [3], Gender Classification [5], Age Classification [4], Animation [6] 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. In geometric feature-based approaches, the features are extracted using anthropometric relation [7], [8] of the face components [9]. Valley detection filters [10] and analysis of horizontal and vertical edge projections are such examples [11]. Functions which are used in this approach are not one-to-one transforms; furthermore, these methods cannot distinct slight changes in the image very well. Template-based approaches match facial components using appropriate energy functional. The best match of a template in the facial image will yield the minimum energy. Template matching [12], Deformable templates [13], ASM [14] and genetic algorithms [15] fall into this category. These methods are time consuming and lost their efficiency due to variation in the illumination and out of axis rotation. In addition, these algorithms fail when the size of pattern is constant and the size of feature changes. Color segmentation techniques [16], [23], on the other hand, use skin color to isolate the face. Any non-skin color region within the face can be represented as a candidate for “eyes” and/or “mouth”. This method... ... middle of paper ... ...n orthogonal projection operator on a vector space is any operator that maps each vector into its orthogonal projection on a hyper-plane (line in ℝ 2 and plane in ℝ 3) through the origin. (Refer to Figure 1a) Figure 1b shows that the orthogonal projection is not one-to-one. Therefore, all distinct points on the same vertical line are mapped into the same point in the xy-plane. This defect causes loss of structure in the data and the feature extraction process may be faced with problems. Figure 2 shows the result of applying horizontal projection function on image X. As it is obvious, the main horizontal change in the picture is occurred in the line 3 (X3), but the projection cannot highlight that. We proposed a linear transformation to cover this weakness. A linear transformation T maps space V to W. Therefore, we need defining a space for representing an image.
It is easy to think of biometrics as the future science technology are always happened in some fictions, associated with solar car and clones together. In fact, it has long history that people understood the basic principle and application of the biometric. Thousands of years ago, the people of the Nile basin used the biometric in everyday transactions (such as scarring, skin tone, eye color, height, etc.) for identification. Of course, they had no any automatic electronic identification system, or computer network, but the principle is similar.
Dog’s vision and their eyes structure are enormous difference than human beings. Although, dogs could only acquaint limited colors, majority of their vision abilities did not be affected. In Miller and Murphy’s study (2002), they mentioned that dogs only have two types of cones which performed a color identification as a primary task. One of the cone cells could receive light wave
Retinal vessel segmentation is important for the diagnosis of numerous eye diseases and plays an important role in automatic retinal disease screening systems. Automatic segmentation of retinal vessels and characterization of morphological attributes such as width, length, tortuosity, branching pattern and angle are utilized for the diagnosis of different cardiovascular and ophthalmologic diseases. Manual segmentation of retinal blood vessels is a long and tedious task which also requires training and skill. It is commonly accepted by the medical community that automatic quantification of retinal vessels is the first step in the development of a computer-assisted diagnostic system for ophthalmic disorders. A large number of algorithms for retinal vasculature segmentation have been proposed. The algorithms can be classified as pattern recognition techniques, matched filtering, vessel tracking, mathematical morphology, multiscale approaches, and model based approaches. The first paper on retinal blood vessel segmentation appeared in 1989 by Chaudhuri et al. [21]...
Human pigmentation is influenced by hemoglobins within blood vessels in the skin, carotene and melanins. Melanin, the basis of pigmentation, can be found in the forms of eumelanin and phaeomelanin. Eumelanin is the brown-black pigment located in the skin, hair, and eyes. Phaeomelanin is a yellow to reddish-brown pigment found in small quantities within the skin, eyes, and red hair. Because of these two pigments, to a greater or lesser degree, we have the variation in human pigmentation that is seen today.
...omated detection of lines and points in the images and the use of smart markers in reference video recordings.
The most predominant feature of the human face is eyes. When talking to a person our eyes meet there eyes; the way that people identify each other is through eyes; eyes even have the power to communicate on its own. Eliezer identified people buy there eyes and knew their emotions through their eyes. “Across the aisle, a beautiful women with dark hair and dreamy eyes. I had
A person’s face is tremendously expressive when it comes to emotions, a person’s face is able to display a numerous amount of emotions worth almost a thousand words. And unlike some forms of nonverbal communication, facial expressions are universal worldwide. Facial expressions for feelings anger, disgust, fear, joy, sadness, surprise and many more are the same all around the world.
...mation about colorblindness and color deficiencies. Color wheel images used in this paper were taken from this site.
Hirayama, T., Iwai, Y., & Yachida, M. (2007, May). Integration of facial position estimation and person identification for face authentication [Electronic Version]. Systems & Computers in Japan, 38(5), 43-58.
By definition, “biometrics” (Woodward, Orlans, and Higgins, 2003) is the science of using biological properties to identify individuals; for example, fingerprints, retina scans, and voice recognition. We’ve all seen in the movies, how the heroes and the villains have used other’s fingerprints and voice patterns to get into the super, secret vault. While these ideas were fantasy many years ago, today biometrics are being used and you may not even know it.
[5] W.Zhang, S.Shan, ”Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition,” ICCV, vol. 1, pp.786-791, 2005.
It was determined that infants develop color vision at or around three months of age and that when final results were evaluated and compared to adult (only) measures, actually have better quality color vision (Brown et al., 1994). An interesting study by Chase (1937) made efforts to discover the identities of color in which infants that aged 2 to 10 weeks old were tested to find out what colors they could perceive. The results they came up with were that very young infants could tell the difference between the primary colors and combinations but there were numerous limitations to the study (Chase, 1937). The study had placed infants to lie down and view a screen while observing eye movements (Chase, 1937). Findings by Franklin, Pilling, and Davies (2005) explain that color categorizing occurs in four month old infants and adults alike. A study by Bornstein, Kessen, & Weiskopf (1976) has supporting evidence that color is categorized in 4 month old infants and determined the boundaries within...
[Jain, 2004] Jain, A.K.;Ross, A.;Prabhakar, S.;"An introduction to biometric recognition", Volume: 14 Issue: 1 Issue Date: Jan. 2004, on page(s): 4 - 20
By searching correct feature point and setting bidirectional threshold value,the matching process can be quickly and precisely implemented with optimistic result. The resemblance of two images is defined as the overall similarity between two families of image features[1]. Same proportion image matching algorithm using bi-directional threshold image matching technique is used. Small window of pixels in a reference image (template) is compared with equally sized windows of pixels in other (target) images. In FBM, instead of matching all pixels in an image, only selected points with certain features are to be matched. Area based matching provide low speed. feature based matching algorithm is faster in comparison to the area based matching technique. feature based matching time complexity depend on number of feature to be selected as well as right or wrong threshold. If the number of feature are high then sometimes it takes more computational time in comparison to area based feature. The number of features extracted from an image depends largely on the contents of an image. If there are high variations then features computed are high. This reduces time efficiency to
Iris recognition is very accurate and distinctive because iris has a complex texture that can produce a substantial amount of information to identify a person. Furthermore, the iris remains almost unchanged from childhood, only minuscule variations are presented. The biometric data is captured using a small and high definition camera that is able to recognize different characteristics of the iris. Moreover, the system can detect the use of contact lens with a fake iris and can realize with the natural movement of the eye if the sample object is a living being. Although initially iris recognition systems were expensive and complex to use, new technology developments have improved these weaknesses.