發(fā)布時(shí)間：2018-01-20 04:02

  本文關(guān)鍵詞： 遺傳算法 特征表情識(shí)別 Pareto優(yōu)化 隨機(jī)森林 uniform LGBP 三維人臉形變模型 ISOMAP算法　出處：《東華大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：人的大腦具備天生的人臉識(shí)別能力,可以輕易地分辨出不同的人。但是機(jī)器自動(dòng)識(shí)別人臉技術(shù)的發(fā)展卻面臨巨大的挑戰(zhàn)。在實(shí)際環(huán)境中,二維人臉識(shí)別不可避免地受到光照、姿態(tài)和表情的影響,這些因素已成為了二維人臉識(shí)別技術(shù)向前發(fā)展的最大障礙。同時(shí)由于三維人臉形變模型的提出使得三維人臉表情識(shí)別變得非常熱門,如何建立魯棒快速的三維人臉表情識(shí)別模型成為了人臉識(shí)別中一大挑戰(zhàn)。本文基于改進(jìn)的遺傳和Pareto優(yōu)化算法對(duì)人臉表情進(jìn)行識(shí)別以提高人臉表情識(shí)別的準(zhǔn)確率。首先使用Haar-like特征表示方法和雙邊濾波器對(duì)人臉圖片進(jìn)行預(yù)處理。其次,利用uniform LGBP方法對(duì)人臉的特征進(jìn)行提取,降低特征維數(shù)。再次,改進(jìn)GA適應(yīng)度函數(shù),提出新的Pareto目標(biāo)函數(shù)并采用改進(jìn)的GA和Pareto優(yōu)化算法對(duì)最優(yōu)的顯著特征進(jìn)行挑選。最后,使用隨機(jī)森林分類器對(duì)人臉特征進(jìn)行分類。在三維人臉表情識(shí)別中,通過將三維人臉頂點(diǎn)映射到二維空間中,然后使用二維人臉表情識(shí)別的方法進(jìn)行識(shí)別。最后與現(xiàn)有的算法進(jìn)行比較試驗(yàn),實(shí)驗(yàn)表明,本文所提出的人臉表情識(shí)別算法在識(shí)別精度和計(jì)算時(shí)間上都優(yōu)于現(xiàn)有文獻(xiàn)中提出的算法。本文主要的創(chuàng)新點(diǎn)有:1)提出uniform LGBP對(duì)人臉特征進(jìn)行提取的方法,有效地降低了提取特征的維數(shù)。2)提出將GA和Pareto算法結(jié)合,對(duì)最優(yōu)顯著特征進(jìn)行挑選,為了提升人臉表情識(shí)別的精度,改進(jìn)GA的適應(yīng)度進(jìn)化函數(shù),并提出了兩個(gè)新的Pareto優(yōu)化算法的目標(biāo)函數(shù)來刻畫最小化類內(nèi)變化和最大化類間變化。3)基于魯棒的三維人臉形變模型,提出加入正則化項(xiàng)的三維人臉形狀擬合目標(biāo)函數(shù),使三維人臉形狀不會(huì)產(chǎn)生過擬合問題。4)提出使用ISOMAP算法將三維人臉的頂點(diǎn)映射到二維空間中,可以有效地對(duì)三維頂點(diǎn)內(nèi)在的幾何結(jié)構(gòu)進(jìn)行學(xué)習(xí),使映射得到的二維人臉不存在形變問題。
[Abstract]:The human brain has the natural ability of face recognition, it can easily distinguish different people, but the development of the machine automatic face recognition technology is facing a huge challenge. In the actual environment. Two-dimensional face recognition is inevitably influenced by illumination, pose and expression. These factors have become the biggest obstacle to the development of two-dimensional face recognition technology. At the same time, 3D facial expression recognition has become very popular due to the proposed three-dimensional face deformation model. How to establish a robust and fast 3D facial expression recognition model has become a major challenge in face recognition. This paper presents an improved genetic and Pareto algorithm for facial expression recognition in order to improve facial expression recognition. First, the Haar-like feature representation method and bilateral filter are used to preprocess the face image. Uniform LGBP method is used to extract face features to reduce the feature dimension. Thirdly, the GA fitness function is improved. A new Pareto objective function is proposed and the improved GA and Pareto optimization algorithms are used to select the salient features of the optimization. Finally. A random forest classifier is used to classify facial features. In 3D facial expression recognition, 3D face vertices are mapped to two-dimensional space. Then the two-dimensional facial expression recognition method is used to recognize. Finally, compared with the existing algorithms, the experiment shows that. The facial expression recognition algorithm proposed in this paper is superior to the one proposed in the literature in terms of recognition accuracy and computation time. The main innovation of this paper is: 1). A method of facial feature extraction based on uniform LGBP is proposed. In order to improve the accuracy of facial expression recognition, GA and Pareto algorithms are combined to select the optimal salient features. The fitness evolution function of GA is improved. The objective functions of two new Pareto optimization algorithms are proposed to characterize the 3D face deformation model based on robust algorithm. The objective function of 3D face shape fitting with regularization term is proposed, so that the 3D face shape will not be overfitted. (4) A ISOMAP algorithm is proposed to map the vertex of 3D face to two-dimensional space. The inherent geometric structure of 3D vertices can be effectively studied, so that there is no deformation problem in the two-dimensional face generated by mapping.
【學(xué)位授予單位】：東華大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 孫蔚;王波;;人臉表情識(shí)別綜述[J];電腦知識(shí)與技術(shù);2012年01期

2 楊梅娟;;人臉表情識(shí)別綜述[J];甘肅科技;2006年04期

3 劉曉e，

本文編號(hào)：1446765