遗传算法在人脸识别特征选择中的应用 外文翻译.doc
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遗传算法在人脸识别特征选择中的应用 外文翻译,遗传算法在人脸识别特征选择中的应用达尔亚 俄泽坎土耳其毕尔坎特大学，计算机工程部，安卡拉摘要人脸识别对计算机视觉问题一直是个挑战。为了解决这个这个问题，筛选功能[1]已被用于[2] 。不过，自从筛选特征被定位于物体识别以来，我们需要选择最适合在面部识别的问题。因此，在本文中，我们使用遗传算法来选择最重要的特点来进行人脸...
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遗传算法在人脸识别特征选择中的应用
达尔亚 俄泽坎
土耳其毕尔坎特大学，计算机工程部，安卡拉
摘要
人脸识别对计算机视觉问题一直是个挑战。为了解决这个这个问题，筛选功能[1]已被用于[2] 。不过，自从筛选特征被定位于物体识别以来，我们需要选择最适合在面部识别的问题。因此，在本文中，我们使用遗传算法来选择最重要的特点来进行人脸识别。
关键词：遗传算法，特征选择，人脸识别，筛选功能
1. 引言
本文中，我们的目标是选择最有用的特征，并将它应用于人脸识别中。基于这样的目的，我们使用遗传算法去学习筛选功能的某种特征[1]。它能够应用到目标识别，能够对人脸的某个特点进行描述。
使用筛选功能进行脸部识别已被提议在[2]中。我们相信找到这些在人脸识别中十分有用的特征的共同点将对人脸识别产生极好的结果。因为我们减少了不必要的特征，所以这将极大的减少计算机的识别时间。
首先我们在a部分中给出了筛选功能和人脸识别问题的相关信息；在b部分中给出了遗传算法的方法。在第2条中我们介绍了我们如何利用遗传算法来选择最佳的功能进行人脸识别。第3条中则给出了实验结果，并在进行了简要的总结概括后，对算法的可扩展性做了一些未来性的工作。
Feature Selection for Face Recognition
Using a Genetic Algorithm
Derya Ozkan
Bilkent University, Department of Computer Engineering
Turkey, Ankara
ABSTRACT
Face recognition has been one of the challenging problems of computer vision.Inresponse to this problem, SIFT features [1] have been used in [2]. However, since SIFT features were addressed to object recognition; we need to select the features that best fits in the face recognition problem. So, in this paper, we are using a genetic algorithm to select the most important features for face recognition.
Keywords: Genetic Algorithm, Feature Selection, Face Recognition, SIFT Features
1. INTRODUCTION
In this paper, we aim to select the most useful features for face recognition. For this purpose, we use a genetic algorithm to learn which features of SIFT features [1], used in object recognition, can describe an interest point of the face.
A face recognition approach using the SIFT features has been proposed in [2]. We believe that finding the subset of those features, which are more useful for face recognition,will lead to better results for the face recognition problem. It will reduce the computation time since we remove the unnecessary features.
We first give information about SIFT features and the problem of face recognition in part a; and the genetic algorithm approach in part b. In section 2, we show how we can use genetic algorithm to select the best features for face recognition. Section 3 gives the experimental results; and in conclusion after a summary, we give future works that can be done to extend the algorithm.
达尔亚 俄泽坎
土耳其毕尔坎特大学，计算机工程部，安卡拉
摘要
人脸识别对计算机视觉问题一直是个挑战。为了解决这个这个问题，筛选功能[1]已被用于[2] 。不过，自从筛选特征被定位于物体识别以来，我们需要选择最适合在面部识别的问题。因此，在本文中，我们使用遗传算法来选择最重要的特点来进行人脸识别。
关键词：遗传算法，特征选择，人脸识别，筛选功能
1. 引言
本文中，我们的目标是选择最有用的特征，并将它应用于人脸识别中。基于这样的目的，我们使用遗传算法去学习筛选功能的某种特征[1]。它能够应用到目标识别，能够对人脸的某个特点进行描述。
使用筛选功能进行脸部识别已被提议在[2]中。我们相信找到这些在人脸识别中十分有用的特征的共同点将对人脸识别产生极好的结果。因为我们减少了不必要的特征，所以这将极大的减少计算机的识别时间。
首先我们在a部分中给出了筛选功能和人脸识别问题的相关信息；在b部分中给出了遗传算法的方法。在第2条中我们介绍了我们如何利用遗传算法来选择最佳的功能进行人脸识别。第3条中则给出了实验结果，并在进行了简要的总结概括后，对算法的可扩展性做了一些未来性的工作。
Feature Selection for Face Recognition
Using a Genetic Algorithm
Derya Ozkan
Bilkent University, Department of Computer Engineering
Turkey, Ankara
ABSTRACT
Face recognition has been one of the challenging problems of computer vision.Inresponse to this problem, SIFT features [1] have been used in [2]. However, since SIFT features were addressed to object recognition; we need to select the features that best fits in the face recognition problem. So, in this paper, we are using a genetic algorithm to select the most important features for face recognition.
Keywords: Genetic Algorithm, Feature Selection, Face Recognition, SIFT Features
1. INTRODUCTION
In this paper, we aim to select the most useful features for face recognition. For this purpose, we use a genetic algorithm to learn which features of SIFT features [1], used in object recognition, can describe an interest point of the face.
A face recognition approach using the SIFT features has been proposed in [2]. We believe that finding the subset of those features, which are more useful for face recognition,will lead to better results for the face recognition problem. It will reduce the computation time since we remove the unnecessary features.
We first give information about SIFT features and the problem of face recognition in part a; and the genetic algorithm approach in part b. In section 2, we show how we can use genetic algorithm to select the best features for face recognition. Section 3 gives the experimental results; and in conclusion after a summary, we give future works that can be done to extend the algorithm.
