本論文的目的是利用人臉在彩色影像中所提供的多色彩空間資訊，來達成在變異度較大的光源中即時偵測人臉的任務。彩色影像所擁有的原始RGB色彩資訊，經過轉化到正規RGB以及HSV (色調、飽合、明度)等色彩空間後，擁有對光源變化反應減緩的特性。以此特性為基礎，在４個選定的色彩空間中定義８種不同的類赫爾特徵(Haar-like feature)，再利用推進演算法（Boosting algorithm）選出重要性最高的幾組特徵來進行對人臉的特徵。實驗結果顯示依此方法所產生的辨識器可在２點多秒內處理近百萬個次窗口(sub-window)，並對光源變化有相當程度的抵抗力。 / The main goal of this thesis is to detect human face under varying lighting condition by utilizing multiple color space information in real-time. Images of RGB color space can be converted into normalized RGB and HSV color spaces and thus reduce the interference of lighting condition. Base on this mechanism, we define 8 Haar-like features inside 4 selected color spaces, and then select the important features with boosting algorithm. Experimental results show that detectors constructed with our approach are able to process nearly one million sub-windows within 2.4 seconds, being robust to the changes of lighting conditions.

除了文字訊息，張貼相片也是臉書使用者常用的功能，這些上傳的照片種類繁多，可能是自拍照、風景照、或食物照等等，本論文的研究以影像分析為出發點，探討相片內容跟發佈者間之關係，希望藉由相片獲得的資訊，輔助分析使用者樣貌。 本研究共收集32位受測者上傳至臉書的相片，利用電腦視覺技術分析圖像內容，如人臉偵測、環境識別、找出影像上視覺顯著的區域等，藉由這些工具所提供的資訊，將照片加註標籤，以及進行自動分類，並以此兩個層次的資訊做為特徵向量，利用階層式演算法進行使用者分群，再根據實驗結果去分析每一群的行為特性。 透過此研究，可對使用者進行初步分類、瞭解不同的使用者樣貌，並嘗試回應相關問題，如使用者所張貼之相片種類統計、不同性別使用者的上傳行為、 依據上傳圖像內容，進行使用者樣貌分類等，深化我們對於臉書相片上傳行為的理解。 / Apart from text messages, photo posting is a popular function of Facebook. The uploaded photos are of various nature, including selfie, outdoor scenes, and food. In this thesis, we employ state-of-the-art computer vision techniques to analyze image content and establish the relationship between user profile and the type of photos posted. We collected photos from 32 Facebook users. We then applied techniques such as face detection, scene understanding and saliency map identification to gather information for automatic image tagging and classification. Grouping of users can be achieved either by tag statistics or photo classes. Characteristics of each group can be further investigated based on the results of hierarchical clustering. We wish to identify profiles of different users and respond to questions such as the type of photos most frequently posted, gender differentiation in photo posting behavior and user classification according to image content, which will promote our understanding of photo uploading activities on Facebook.

在電腦視覺的研究之中，有關物件的偵測與追蹤應用在速度及可靠性上的追求一直是相當具有挑戰性的問題，而現階段發展以視覺為基礎互動式的應用，所使用到技術諸如:類神經網路、SVM及貝氏網路等。 本論文中我們持續深入此領域，並提出及發展一個方向性邊緣特徵集(DEM)與修正後的AdaBoost訓練演算法相互結合，期能有效提高物件偵測與識別的速度及準確性，在實際驗證中，我們將之應用於多種角度之人臉偵測，以及臉部表情識別等兩個主要問題之上；在人臉偵測的應用中，我們使用CMU的臉部資料庫並與Viola & Jones方法進行分析比較，在準確率上，我們的方法擁有79% 的recall及90% 的precision，而Viola & ones的方法則分別為81%及77%；在運算速度上，同樣處理512x384的影像，相較於Viola & Jones需時132ms，我們提出的方法則有較佳的82ms。 此外，於表情識別的應用中，我們結合運用Component-based及Action-unit model 兩種方法。前者的優勢在於提供臉部細節特徵的定位及追蹤變化，後者主要功用則為進行情緒表情的分類。我們對於四種不同情緒表情的辨識準確度如下：高興(83.6%)、傷心(72.7%)、驚訝(80%) 、生氣(78.1%)。在實驗中，可以發現生氣及傷心兩種情緒較難區分，而高興與驚訝則較易識別。 / Rapid and robust detection and tracking of objects is a challenging problem in computer vision research. Techniques such as artificial neural networks, support vector machine and Bayesian networks have been developed to enable interactive vision-based applications. In this thesis, we tackle this issue by devising a novel feature descriptor named directional edge maps (DEM). When combined with a modified AdaBoost training algorithm, the proposed descriptor can produce effective results in many object detection and recognition tasks. We have applied the newly developed method to two important object recognition problems, namely, face detection and facial expression recognition. The DEM-based methodology conceived in this thesis is capable of detecting faces of multiple views. To test the efficacy of our face detection mechanism, we have performed a comparative analysis with the Viola and Jones algorithm using Carnegie Mellon University face database. The recall and precision using our approach is 79% and 90%, respectively, compared to 81% and 77% using Viola and Jones algorithm. Our algorithm is also more efficient, requiring only 82 ms (compared to 132 ms by Viola and Jones) for processing a 512x384 image. To achieve robust facial expression recognition, we have combined component-based methods and action-unit model-based approaches. The component-based method is mainly utilized to locate important facial features and track their deformations. Action-unit model-based approach is then employed to carry out expression recognition. The accuracy of classifying different emotion type is as follows: happiness 83.6%, sadness 72.7%, surprise 80%, and anger 78.1%. It turns out that anger and sadness are more difficult to distinguish, whereas happiness and surprise expression have higher recognition rates.