本文關(guān)鍵詞：基于深度學(xué)習(xí)的人臉表情識(shí)別　出處：《浙江理工大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 人臉表情識(shí)別 特征提取 深度學(xué)習(xí) 深度信念網(wǎng)絡(luò) 魯棒性

【摘要】：人臉表情識(shí)別是當(dāng)前計(jì)算機(jī)視覺(jué)、模式識(shí)別、人工智能等領(lǐng)域的熱點(diǎn)研究課題。它是智能人機(jī)交互技術(shù)中的一個(gè)重要組成部分，近年來(lái)得到廣泛的關(guān)注，不同領(lǐng)域的研究者提出了許多新方法。本文綜述了國(guó)內(nèi)外近年來(lái)人臉表情識(shí)別技術(shù)的最新發(fā)展?fàn)顩r，對(duì)人臉表情識(shí)別系統(tǒng)所涉及到的關(guān)鍵技術(shù)：人臉表情特征提取和人臉表情分類，分別做了詳細(xì)的分析和歸納。最后，總結(jié)了人臉表情識(shí)別的研究現(xiàn)狀，并指出了其未來(lái)的發(fā)展方向。 本文主要研究了在人臉表情識(shí)別中特征提取和分類中的一些關(guān)鍵問(wèn)題，并結(jié)合深度學(xué)習(xí)的方法提出了一些改進(jìn)方法，最后通過(guò)實(shí)驗(yàn)進(jìn)行了驗(yàn)證。本文的主要工作如下: 1.提出一種融合深度信念網(wǎng)絡(luò)和多層感知器的人臉表情識(shí)別新方法。首先采用深度信念網(wǎng)絡(luò)對(duì)提取的原始人臉表情圖像的初級(jí)特征或局部二元模式（LBP）征進(jìn)行無(wú)監(jiān)督學(xué)習(xí)，得到更高層次的抽象特征，然后將其用于初始化多層感知器模型中的隱層網(wǎng)絡(luò)權(quán)重值，最后利用該初始化后的多層感知器實(shí)現(xiàn)人臉表情的分類。在JAFFE數(shù)據(jù)庫(kù)中，該方法能夠取得最好的人臉表情正確識(shí)別率為90.95%；在Cohn-Kanade數(shù)據(jù)庫(kù)中，，取得了最好98.57%的人臉表情正確識(shí)別率。而且與其它識(shí)別方法相比，深度信念網(wǎng)絡(luò)（DBNs）方法有著明顯的優(yōu)勢(shì)�？梢�(jiàn)，該方法用于人臉表情識(shí)別，可以較好地改善識(shí)別性能。 2.對(duì)深度信念網(wǎng)絡(luò)的魯棒性人臉表情識(shí)別性能做了研究。考慮到在人臉表情識(shí)別過(guò)程中圖像可能受到噪聲的影響，在對(duì)測(cè)試圖像存在像素腐蝕的情況下，著重對(duì)基于深度信念網(wǎng)絡(luò)的魯棒性人臉表情識(shí)別性能進(jìn)行了探討。深度信念網(wǎng)絡(luò)具有很強(qiáng)的無(wú)監(jiān)督學(xué)習(xí)的能力，在不同的腐蝕比例下，仍然能取得不錯(cuò)的識(shí)別效果。在Cohn-Kanade數(shù)據(jù)庫(kù)中，實(shí)驗(yàn)結(jié)果表明DBNs具有優(yōu)越的分類性能和魯棒性，是非常適合于人臉表情識(shí)別的。 3.設(shè)計(jì)了人臉表情識(shí)別的GUI界面。在完成人臉表情識(shí)別的程序設(shè)計(jì)后，根據(jù)GUI系統(tǒng)設(shè)計(jì)的簡(jiǎn)單性、一致性、習(xí)常性，設(shè)計(jì)了人臉表情識(shí)別的GUI界面，方便程序的操作使用。
[Abstract]:Facial expression recognition is the current computer vision, pattern recognition, hot research topics in the field of artificial intelligence. It is an important part of intelligent human-computer interaction technology, has received wide attention in recent years, researchers in different fields and put forward many new methods. This paper reviews the technology of facial expression recognition in recent years. The latest development status and key technologies involved in the system of facial expression recognition, facial feature extraction and facial expression classification, were analyzed and summarized in detail. Finally, summarizes the research status of face recognition, and points out the future direction of development.
This paper mainly studies some key problems in feature extraction and classification of facial expression recognition, and proposes some improvement methods combined with deep learning method. Finally, it is verified by experiments.
1. this paper proposes a new method of facial expression recognition fusion depth of belief network and multilayer perceptron. Firstly, the primary characteristics of the two modes of local or deep belief networks to extract the original facial expression image (LBP) features for unsupervised learning, get a higher level of abstraction features, and then applied to the hidden layer weights initialize the multi-layer perceptron model in the value of the final realization of facial expression classification using multilayer perceptron. After the initialization in the JAFFE database, this method can achieve the best correct facial expression recognition rate was 90.95%; in the Cohn-Kanade database, obtained the correct recognition rate of 98.57%. The best facial expression and compared with other methods. Deep belief network (DBNs) method has obvious advantages. Obviously, the method for facial expression recognition, can effectively improve the recognition performance.
Robust facial expression recognition performance of 2. deep belief networks have been studied. Considering the influence of image by noise in facial expression recognition process, in the presence of corrosion on the pixel test image case, focuses on the robustness of face recognition based on the performance of deep belief networks are discussed. The deep belief network has no the ability of supervised learning is very strong, in corrosion under different proportion, still can achieve good recognition effect. In the Cohn-Kanade database, the experimental results show that DBNs has excellent classification performance and robustness, is very suitable for facial expression recognition.
3., the GUI interface of facial expression recognition is designed. After finishing the program design of facial expression recognition, according to the simplicity, consistency and habit of GUI system design, the GUI interface of facial expression recognition is designed to facilitate the operation and application of programs.

【學(xué)位授予單位】：浙江理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前4條

1 龔婷;胡同森;田賢忠;;基于類內(nèi)分塊PCA方法的人臉表情識(shí)別[J];機(jī)電工程;2009年07期

2 劉曉e

本文編號(hào)：1385880