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. As is evident from Figure5, the hair line, chin, and sides of face are points that form the face boundaries. These points are simultaneously localized while face is detected and cropped from the image. In order to extract other facial feature points two types of geometric feature-based methods are used. First, Linear Principal Transformation (LPT), which is proposed by Dehshibi et al [Deh10], is performed to locate the eyebrows, eyes, nose tip and center line of lips in frontal view of face. Then, an extended version of LPT, which we called it LPT2 are used to locate these points in profile view.
Linear Principal Transformation (LPT) is a one to one transformation, which has three key features, including "accuracy," "power," and "simplicity." The main goal of LPT is to identify the most meaningful basis, which contains the features of interest. It will reveal the hidden structure of data. LPT assumes that an m×n image consists of m observation sets in an n-dimensional vector space. Among these vectors, the vector which has the highest variance corresponds to the feature of interest. To obtain a feature, first, the covariance matrix of image is calc...
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...ing images is calculated. Then, based on the calculated weight half of the training data is eliminated. In the second stage filtering the database is done using the "eye region". In the last stage of recognition the "Frontal face" is used to find three images, which have the minimum Euclidean distance with the input image.
In order to rate the efficiency of the proposed algorithm, a structure for the family should be considered. With respect to the images in the FFIDB, a structure with three levels is defined. As is evident from Figure 11 each level has an impact factor. The efficiency rate of proposed method is equal to(sum of each level impact factor)/(sum of maximum impact factor). For example, if the selected images in recognition phase have the "mother", "sister", and "cousin" relation with the input image, then the accuracy of the algorithm is 77.77 percent.
...ge flow and pattern types, are prominent enough to align fingerprints directly. Nilsson [26] detected the core point by complex filters applied to the orientation field in multiple resolution scales, and the translation and rotation parameters are simply computed by comparing the coordinates and orientation of the two core points. Jain [27] predefined four types of kernel curves:first is arch, second is left loop ,third is right loop and fourth is whorl, each with several subclasses respectively. These kernel curves were fitted with the image, and then used for alignment. Yager [28] proposed a two stage optimization alignment combined both global and local features. It first aligned two fingerprints by orientation field, curvature maps and ridge frequency maps, and then optimized by minutiae. The alignment using global features is fast but not robust, because the
You are more likely to be genetically like someone who looks like you more than someone who does not, because some traits such as skin color and height are determined genetically. Therefore, people who share similar genes look more alike than people with completely different genetic makeups.
According to Guido, Peluso, and Moffa (2011) facial hair is a secondary facial feature; which can play a role when making judgments about others (Reed & Blunk, 1990). In light of this, pass and recent studies have been conducted to investigate this process.
The usage of the iris technology is very fast. The capturing and the testing of the images is very fast, it must require some training. The glasses must be removed during the enrollment in the recognition system will ensure the best image will be captured without any reflections from the glasses or the lenses.
From many points of views, it can be considered as the starting point. The team working with it has a dream to make more objects recognition which is context base. They also have a desire to make the recognition more interactive. A new and exceptional feature has been suggested where a particular part of an image can be tapped and the information can be heard.
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.
Lyons et al. [6] Classifying facial attributes using a 2-D Gabor wavelet representation and discriminant analysis used a set of multi scale, multi orientation Gabor filters to transform the images first. The Gabor coef...
[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.
Fisher discriminants find the line that best separates the points. To identify an input test image, the projected test image is compared to each projected training image, and the test image is identified as the closest training image (Zhao, Chellappa & Phillips, 1999).
Video-based face recognition has the advantage over other trustworthy characteristics for biometric recognition, such as iris and fingerprint scans, that it does not require the cooperation ...
[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
Then classification is performed on the basis of similarity score of a class with respect to a neighbor.
Face recognition: Face recognition is based on the shape and location of the eyes, eyebrows, nose, lips and chin. It is non intrusive method and very popular also. Facial recognition is carried out in two ways [5] [6]: a. Facial metric: The location and shape of facial attributes (e.g. distances between pupils or from nose to lip or chin) are measured. b. Eigen faces: Analyzing the overall face image as “a weighted combination of a number of canonical faces.”
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.