近年來深度學習逐漸成為信息科學領(lǐng)域的研究熱點,而隨著基于深度學習方法的研究技術(shù)的不斷推進,數(shù)據(jù)特征信息的提取和處理效率獲得了極大的提升,同時也推動了深度學習在計算機視覺、語音處理和自然語言處理等相關(guān)領(lǐng)域的迅猛發(fā)展。作為計算機視覺領(lǐng)域中一個比較重要的研究子方向,人臉表情識別可以廣泛地應用到多個領(lǐng)域如人機交互、不良狀態(tài)檢測等。通常來說,表情不僅是一種非語言交際的方式,可以傳遞用于交流的輔助信息,也是人類情緒精神狀態(tài)的潛在反映。通過表情輔助消息傳遞,可以讓消息的信息量更為豐富,而消息接收者也能更為準確地把握信息的特征。所以在人機交互方面,表情識別可以用于讓機器更準確地獲取用戶傳遞的消息內(nèi)容;而在一些需要判斷用戶狀態(tài)的場景,也可以利用表情識別完成不良狀態(tài)的識別,比如判斷駕駛員是否處于疲勞駕駛狀態(tài)。通常非深度學習方法的表情識別主要是利用人工選取的表情特征,包括幾何特征、統(tǒng)計特征和運動特征等,以及分類判別器的決策分類進行表情識別。這些方法都取得了一定的效果,但是過度依賴于特征的人工選取,魯棒性較差,同時計算量非常大。深度學習方法則避免了特征的人工選取,同時其數(shù)據(jù)冗余度也保證了表情識別系統(tǒng)的魯棒性。... 

【文章來源】：哈爾濱工業(yè)大學黑龍江省 211工程院校 985工程院校

【文章頁數(shù)】：113 頁

【學位級別】：碩士

【文章目錄】：
詳細中文摘要
ABSTRACT
CHAPTER 1. INTRODUCTION
    1.1 RESEARCH BACKGROUND AND SIGNIFICANCE
    1.2 RESEARCH STATUS OF RELATED FIELDS
    1.3 MAIN RESEARCH CONTENTS OF THIS SUBJECT
CHAPTER 2. FOUNDATIONS OF CONVOLUTIONAL NEURAL NETWORK ANDOPTIMIZATION
    2.1 INTRODUCTION
    2.2 COMPUTATIONS IN CONVOLUTIONAL NEURAL NETWORK
    2.3 GENERAL MATRIX MULTIPLICATION ALGORITHM FOR CNNACCELERATION
    2.4 WINOGRAD ALGORITHM FOR CONVOLUTIONAL LAYERACCELERATION
    2.5 BRIEF SUMMARY
CHAPTER 3. COMPRESSION AND OPTIMIZATION FOR LONG SHORT-TERMMEMORY
    3.1 INTRODUCTION
    3.2 COMPUTATIONS IN LONG SHORT-TERM MEMORY
    3.3 CLASSICAL DECOMPOSITION METHODS
        3.3.1 Tensor basics
        3.3.2 Singular value decomposition
        3.3.3 Tucker decomposition
    3.4 TENSORIZED COMPRESSION FOR LSTM ACCELERATION
        3.4.1 Tensor train tensor decomposition
        3.4.2 Tensorized compression for LSTM
    3.5 BRIEF SUMMARY
CHAPTER 4. SPATIOTEMPORAL MODEL FOR VIDEO FACIAL EXPRESSIONRECOGNITION
    4.1 INTRODUCTION
    4.2 FEATURE EXTRACTION WITH CNN
    4.3 SPATIOTEMPORAL LSTM MODEL
    4.4 OUR PROPOSED FRAMEWORK FOR FER
    4.5 BRIEF SUMMARY
CHAPTER 5. IMPLEMENTATION ON EDGE DEVICES
    5.1 INTRODUCTION
    5.2 IMPLEMENTATION ON ARM CPU
        5.2.1 ARM architecture and NEON technology
        5.2.2 ARM intrinsic programming
        5.2.3 Workflow overview on ARM CPU
    5.3 IMPLEMENTATION ON EDGE DEVICE 1
        5.3.1 Introduction to RK3399 Pro board
        5.3.2 Neural process unit and systolic arrays
        5.3.3 Workflow overview on RK3399 Pro board
    5.4 IMPLEMENTATION ON EDGE DEVICE 2
        5.4.1 Introduction to Atlas 200 Developer Kit
        5.4.2 Ascend 310 chipset and Da Vinci core
        5.4.3 Workflow overview on Atlas 200 DK board
CHAPTER 6. EXPERIMENTAL RESULTS AND PERFORMANCEANALYSIS
    6.1 INTRODUCTION
    6.2 EVALUATION ON FACIAL EXPRESSION RECOGNITIONDATASETS
    6.3 PERFORMANCE ANALYSIS
CHAPTER 7. CONCLUSIONS
結(jié)論
REFERENCES
PAPERS PUBLISHED IN THE PERIOD OF MASTER EDUCATION
ACKNOWLEDGEMENT



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