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1、苏州大学本科生毕业设计（论文）附件：外文文献资料与中文翻译稿1外文文献资料外文文献资料收集：苏州大学 应用技术学院 11 电子班（学号 1116405021）靳冉International Journal of Artificial Intelligence the second section is a problem statement; the third section face recognition techniques- literature review;the fourth section is the proposed method for feature extracti
2、on form a face image dataset, the fifth division is about the implementation; the second last section shows the results; and the last is the conclusion section.2. PROBLEM STATMENTThe difficulties in face recognition are very real-time and natural. The face image can have head pose problem, illuminat
3、ion problem, facial expression can also be a big problem. Hair style and aging problem can also reduce the accuracy of the system. There can be many other problems such as occlusion, i.e., glass, scarf, etc., that can decrease the performance. Image is a multi-dimension matrix in mathematics that ca
4、n be represented by a matrix value. Image can be treated as a vector having magnitude and direction both. It is known as vector image or image Vector.苏州大学本科生毕业设计（论文）附件：外文文献资料与中文翻译稿4If represents a p x q image vector and x is matrix of image vector. Thus, image matrix can be represented as where t is
5、 transpose of the matrix x. Thus, to identify the glass in an image matrix is very difficult and requires some new approaches that can overcome these limitations. The algorithm proposed in this paper successfully overcomes these limitations. But before that lets see what all techniques have been use
6、d in the field of face identification andface recognition.3. FACE RECOGNITION TECHNIQUES3.1. Face detectionFace detection is a technology to determine the locations and size of a human being face in a digital image. It only detects facial expression and rest all in the image is treated as background
7、 and is subtracted from the image. It is a special case of object-class detection or in more general case as face localizer. Face-detection algorithms focused on the detection of frontal human faces, and also solve the multi-view face detection problem. The various techniques used todetect the face
8、in the image are as below:3.1.1. Face detection as a pattern-classification task:In this face detection is a binary-pattern classification task. That is, the content of a given part of an image is transformed into features, after which a classifier trained on example faces decides whether that parti
9、cular region of the image is a face, or not 3.3.1.2. Controlled background:In this technique the background is still or is fixed. Remove the background and only the faces will be left, assuming the image only contains a frontal face 3.3.1.3. By color:This technique is vulnerable. In this skin color
10、is used to segment the color image to find the face in the image. But this has some drawback; the still background of the same color will also be segmented.3.1.4. By motion:The face in the image is usually in motion. Calculating the moving area will 苏州大学本科生毕业设计（论文）附件：外文文献资料与中文翻译稿5get the face segmen
11、t 3. But this too have many disadvantages as there may be backgrounds which are in motion.3.1.5. Model-based:A face model can contain the appearance, shape, and motion of faces 3. This technique uses the face model to find the face in the image. Some of the models can be rectangle, round, square, he
12、art, and triangle. It gives high level of accuracy if used with some other techniques.3.2. Face RecognitionFace recognition is a technique to identify a person face from a still image or moving pictures with a given image database of face images. Face recognition is biometric information of a person
13、. However, face is subject to lots of changes and is more sensitive to environmental changes. Thus, the recognition rate of the face is low than the other biometric information of a person such as fingerprint, voice, iris, ear, palm geometry, retina, etc. There are many methods for face recognition
14、and to increase the recognition rate. Some of the basic commonly used face recognition techniques are as below:3.2.1. Neural NetworksA neural network learning algorithm called Backpropagation is among the most effective approaches to machine learning when the data includes complex sensory input such
15、 as images,in our case face image. Neural network is a nonlinear network adding features to the learning system. Hence, the features extraction step may be more efficient than the linear Karhunen-Loeve methods which chose a dimensionality reducing linear projection that maximizes the scatter of all
16、projected samples 3. This has classification time less than 0.5 seconds, but has training time more than hour or hours. However, when the number of persons increases, the computing expense will become more demanding 5. In general, neural network approaches encounter problems when the number of classes, i.e., individuals increases.3.2.2. Geometrical Feature MatchingThis technique is based on the set of geometrical features from the image of a face. The overall configuration can be
