臺灣博碩士論文加值系統

社交網路分析一直以來都是個熱門的話題，所有團體成員彼此之間社交關係的集合就是這個團體的社交網路，而透過社交網路分析可以瞭解團體成員之間的互動，這分析可應用在各種與人有關的領域上。在學校裡，學生之間小團體的組成及班級中領導人物與被孤立者的存在，一直都是教育者相當關心的部份。在團體精神治療中，成員之間的交流情況是分析治療成果的指標之一。在網路社群中，瞭解使用者群體之間的互動可以幫助廠商開發更人性化的網路產品。
人格特質分析也是個熱門的話題，每個人的行為都有一套固定的行為模式，而分析這行為模式就是所謂的人格特質分析，這分析也可應用在各種與人有關的領域上。在學校裡，不同類型的學生需要不同方式的教育。在公司面試上，公司透過分析應徵者的人格模式來錄取所需要的人才。在犯罪學中，藉由分析罪犯的行為模式來找出破案的蛛絲馬跡。
然而，一般心理學使用的社交網路分析與人格特質分析都是透過紙筆測驗，使用大量的人力去取得人際互動的資訊，考慮團體成員間友好的互動關係，並使用方向性的連結來表達人們之間的互動關係。目前使用電腦視覺技術的社交網路分析系統，僅考慮人們同時出現頻率當作親密程度的指標，而且使用無方向性的連結來表示人們之間的互動關係。
因此，我們使用擁有電腦視覺技術的多攝影機系統，透過分析人們之間的互動行為，互動行為包含互動的對象、所表達的肢體語言與情緒資訊，根據分析所有的互動得到團體內所有成員之間的社交態度，而這就是這團體的社交網路。除了友好的互動關係之外，我們還考量了厭惡的互動關係，並且使用方向性的連結來表達人們之間的互動，這讓我們的社交網路分析能更貼切現實的互動情況。透過分析一個人所有的社交互動行為，可以得知此人的行為擁有何種傾向，而這行為模式就是這個人的人格特質。
最後，實驗顯示我們可以根據觀察分析人們的互動行為，得到與人們觀察得到的結果大同小異的社交網路分析，證明我們能透過電腦視覺技術取得貼近現實的社交網路分析，並且比起一般心理學的社交網路分析省下許多不必要的人力。

Social network analysis is always a popular topic, and social network is defined as a collection of social interactions between members of group. We can understand social interactions between members through social network analysis, and this analysis can be applied to all people related fields. In school, teachers always take care about sub-groups, leaders and isolates of students. In group psychotherapy, social interactions between members are one indicator of treatment outcomes. In online social, discovering online social network helps contractors developing user-friendly products.
Personality analysis is also a popular topic, and a person’s behavior style is called his personality. Personality analysis also can be used to all people related fields. In school, students who have different behavior styles need different education. In interview, companies employ people who have the personality that they need. In criminology, analyzing criminal’s behavior style helps for solving a criminal case.
However, psychological social network analysis and personality analysis use written tests and a lot labor to get information of friendly social interactions, and present social interactions with directional relations. Social network analysis based on technology of computer vision only uses the frequency of people appear together as feature of social relation, and presents social interactions with non-directional relations. Therefore, we employ a multi-camera system with technology of computer vision to analyze people’s social behaviors. A social behavior consists of a target, body sign and emotion information. Through analyzing people’s social interactions, we can discover people’s social attitudes to other members, and these attitudes construct the social network. Beside friendly relations, we also consider about hostile relations, and we use directional relations to present people’s social interactions. They make our social network analysis closer to reality. Through analyzing a person’s all social behaviors, we can discover his tendency of behaviors, and it’s the personality.
Finally, experiments show that we can discover social network and personality through analyzing people’s social interactions, and the results of analysis are similar to ground truth made by people observing. Besides, we can save a lot labor than psychological social network analysis and personality analysis.

致謝 i
摘要 ii
Abstract iii
Contents iv
List of Figures vi
List of Tables vii
Chapter 1. Introduction 1
Chapter 2. Cameras Configuration 5
2.1 Selection of the Scene with Max Separation 5
2.2 Face Image Capture 7
Chapter 3. Social Behavior Detection 9
3.1 Target Determination 9
3.2 Posture Recognition 10
3.2.1 Posture Recognition Method 11
3.2.2 Body Features Extraction 13
3.3 Emotion Recognition 15
3.3.1 Facial Expression Recognition Method 16
3.3.2 Facial Feature Extraction 17
Chapter 4. Social Network and Personality Analysis 21
4.1 Social Network Analysis 21
4.1.1 Social Interaction Evaluation 21
4.1.2 Social Network Illustration 22
4.2 Personality Analysis 23
4.2.1 Psychological Personality Analysis 24
4.2.2 Personality Evaluation and Illustration 25
Chapter 5. Experimental Results and Discussion 29
5.1 Social Network Analysis 32
5.2 Personality Analysis 37
Chapter 6. Conclusions and Future Works 39
References 41

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