[雙語(yǔ)翻譯]人臉識(shí)別外文翻譯—人臉識(shí)別技術(shù)綜述（節(jié)選）

1、字?jǐn)?shù)：英文 字?jǐn)?shù)：英文 3186 3186 單詞， 單詞，17705 17705 字符；中文 字符；中文 5317 5317 漢字 漢字出處： 出處：V.Vijayakumari.Face V.Vijayakumari.Face Recognition Recognition Techniques: Techniques: A Survey[J]World Survey[J]World Journal Journal of of Com

2、puter Computer Application Application and and Technology.2013,1(2):41-50 Technology.2013,1(2):41-50外文文獻(xiàn) 外文文獻(xiàn) Face Recognition Techniques: A SurveyAbstract Face is the index of mind. It is a complex multidimensional str

3、ucture and needs a good computing technique for recognition. While using automatic system for face recognition, computers are easily confused by changes in illumination, variation in poses and change in angles of faces.

4、A numerous techniques are being used for security and authentication purposes which includes areas in detective agencies and military purpose. These surveys give the existing methods in automatic face recognition and for

5、mulate the way to still increase the performance.Keywords: Face Recognition, Illumination, Authentication, Security1.IntroductionDeveloped in the 1960s, the first semi-automated system for face recognition required the a

6、dministrator to locate features ( such as eyes, ears, nose, and mouth) on the photographs before it calculated distances and ratios to a common reference point, which were then compared to reference data. In the 1970s, G

7、oldstein, Armon, and Lesk used 21 specific subjective markers such as hair color and lip thickness to automate the recognition. The problem with both of these early solutions was that the measurements and locations were

8、manually computed. The face recognition problem can be divided into two main stages: face verification (or authentication), and face identification (or recognition).The detection stage is the first stage; it includes alg

9、orithms. The facial feature detection method proposed by Brunelli and Poggio uses a set of templates to detect the position of the eyes in an image, by looking for the maximum absolute values of the normalized correlatio

10、n coefficient of these templates at each point in test image. To cope with scale variations, a set of templates at different scales was used.The problems associated with the scale variations can be significantly reduced

11、by using hierarchical correlation. For face recognition, the templates corresponding to the significant facial feature of the test images are compared in turn with the corresponding templates of all of the images in the

12、database, returning a vector of matching scores computed through normalized cross correlation. The similarity scores of different features are integrated to obtain a global score that is used for recognition. Other simil

13、ar method that use correlation or higher order statistics revealed the accuracy of these methods but also their complexity.Beymer extended the correlation based on the approach to a view based approach for recognizing fa

14、ces under varying orientation, including rotations with respect to the axis perpendicular to the image plane(rotations in image depth). To handle rotations out of the image plane, templates from different views were used

15、. After the pose is determined ,the task of recognition is reduced to the classical correlation method in which the facial feature templates are matched to the corresponding templates of the appropriate view based models

16、 using the cross correlation coefficient. However this approach is highly computational expensive, and it is sensitive to lighting conditions.2.4.Matching Pursuit Based MethodsPhilips introduced a template based face det

17、ection and recognition system that uses a matching pursuit filter to obtain the face vector. The matching pursuit algorithm applied to an image iteratively selects from a dictionary of basis functions the best decomposit

18、ion of the image by minimizing the residue of the image in all iterations. The algorithm describes by Philips constructs the best decomposition of a set of images by iteratively optimizing a cost function, which is deter