本文關(guān)鍵詞： 表情識別 深度學習 特征提取 人臉檢測 人臉關(guān)鍵點檢測　出處：《西南科技大學》2017年碩士論文　論文類型：學位論文

【摘要】：人臉表情識別是機器視覺和模式識別等領(lǐng)域的一大富有挑戰(zhàn)性的研究課題。人臉表情識別一直作為領(lǐng)域內(nèi)熱門的研究方向,具有廣泛的應(yīng)用場景,例如人機交互、安全監(jiān)控,謊言檢測等等。隨著相關(guān)領(lǐng)域技術(shù)的發(fā)展,特別是自2006年以來深度學習(Deep Learning)技術(shù)的飛速發(fā)展,省去了人工設(shè)計和提取特征的步驟,換之以大數(shù)據(jù)樣本訓練和自動學習有效特征,從而大幅度提高算法模型準確度和適應(yīng)性。因此,結(jié)合深度學習進行人臉表情識別也成為當下熱點之一。本文針對二維人臉圖像進行表情分類識別研究。提出一種基于降噪自編碼器的人臉表情識別算法,在提高識別準確率的同時能夠有效降低表情之間的干擾程度;設(shè)計一種輕量級的卷積神經(jīng)網(wǎng)絡(luò),能夠快速和較為有效地識別人臉表情;提出結(jié)合卷積神經(jīng)網(wǎng)絡(luò)和循環(huán)神經(jīng)網(wǎng)絡(luò)的表情識別方法,針對人臉圖像序列來識別表情。同時,提出一種數(shù)據(jù)篩選框架和兩種較為簡單的數(shù)據(jù)篩選方法,來對數(shù)據(jù)數(shù)據(jù)進行預處理。
[Abstract]:Facial expression recognition is a challenging research topic in the fields of machine vision and pattern recognition. As a hot research direction in the field, facial expression recognition has a wide range of applications, such as human-computer interaction, security monitoring, etc. With the development of technology in related fields, especially the rapid development of deep learning technology since 2006, the steps of artificial design and feature extraction have been eliminated, and big data sample training and automatic learning effective features have been replaced. Thus greatly improving the accuracy and adaptability of the algorithm model. Facial expression recognition based on depth learning has become one of the hotspots. In this paper, facial expression recognition algorithm based on de-noising self-encoder is proposed. At the same time, it can effectively reduce the degree of interference between expressions, and design a lightweight convolution neural network, which can recognize facial expressions quickly and effectively. An expression recognition method based on convolutional neural network and cyclic neural network is proposed to recognize facial expression for face image sequence. At the same time, a data screening framework and two simple data selection methods are proposed. To preprocess the data.
【學位授予單位】：西南科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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