早期的全自動人臉性別辨識系統受限於不成熟的人臉偵測技術。近年來人臉偵測技術逐漸地成熟，因此人臉性別辨識也開始受到重視，並可應用於廣告和監控領域中。
在人臉偵測方面，若整個系統只使用同一種特徵系列將造成偵測率與錯誤率受到該特徵的限制，因此本論文使用三個完全不同特性的分類器串聯而成，第一、二層使用基於統計的人臉遮罩和區塊總和濾除非人臉，第三層使用基於矩形特徵的AdaBoost強分類器，第一層的任務是迅速排除大量非人臉區塊，使整體處理速度能達到即時處理。偵測到人臉後即可進行人臉性別辨識。
在人臉性別辨識方面，前人的方法只單獨考慮空間域或頻率域的分佈，但人臉偵測器取得的人臉同時包含一些空間域與頻率域的非人臉雜訊。有鑑於前人方法所具有的問題，本論文提出一個機制用以刪除或降低空間域與頻率域中的非人臉雜訊，刪除這些對人臉性別辨識無幫助的資訊將能提高識別率與處理效率。本論文首先提出基於平均人臉差異的性別遮罩，其目的是為了刪除空間域中包含的一些干擾資訊或非人臉雜訊。接著使用二維離散餘弦轉換(2D-DCT)刪除高頻雜訊，如此能保留適當低頻能量形成較小維度且包含較多資訊的性別特徵向量。最後使用支持向量機(SVM)辨識所得的性別特徵向量。
為了能即時處理，本論文分析整個系統所有階段的處理速度最佳化策略。在人臉性別辨識的實驗階段，本論文使用FERET人臉資料庫進行識別率、處理速度和訓練時間的比較，比較的方法包含提出的方法與四種前人提出的方法，實驗結果顯示提出的方法識別率最高、訓練時間最短和介於中間的處理速度。
最後本論文使用PTZ攝影機、USB影像擷取卡、筆記型電腦和RS232傳輸線完成一套可用於監控的自動人臉性別辨識系統，此系統可使用介面化程式下達控制命令，並經由RS232傳輸線控制PTZ攝影機的許多功能，包括開關機、旋轉、縮放和背光補償等。

Early full-automatic face gender recognition systems are quite limited since they were just developed from the original face detection technology. However the face detection techniques have been gradually getting precise and reliable in recent years, so other advanced trends, such as face gender recognition has become an interesting topic. Some of the most practical applications of face gender recognition are in public advertisement and surveillance.
In face detection phase, if the whole system uses only one feature series, the detection rate is limited as it is only determined by this feature series. To overcome this problem, this thesis discusses connecting three classifiers of totally different features in series. The first two classifying layers use statistics-based face mask, and the sum of the block filters out non-face features. The third layer uses rectangle-feature-based AdaBoost strong classifier. The objective of the first layer is that fast filtering a large amount of non-face features, which enhances the overall processing speed to reach real-time level. The face gender detection phase is then carried on after the system successfully detects faces.
In face gender recognition phase, previous methods only consider the distribution of the space domain or frequency domain. However, the obtained face from the detector includes non-face noise of some spatial domain and frequency domain at the same time. This paper proposes a mechanism to delete or reduce non-face noise in the spatial domain and frequency domain. Deleting the information that are ineffective or even causing noise to face gender recognition can improve recognition rate and precision. The proposed scheme first employ average-face-difference-based gender mask. Its purpose is to delete some interference information or non-face noise included in the spatial domain. The next step is to apply the 2D-DCT to truncate high-frequency noise, thus the energy is kept at appropriate low frequency. The gender feature vector not only has smaller dimension but also includes more useful information. Finally, the gender is classified with SVM using gender feature vector.
To achieve real-time processing speed, this paper analyzes the speed optimization tactics at all stages. In the experiments, the FERET face database is employed for performance testing, including recognition rate, processing speeds and training time. The methods for comparisons include the proposed method and the four former proposed methods. Experimental result shows that the proposed method provides the best recognition rate with shortest training time, while keeping the similar processing speed.
The equipments used in this research including PTZ camera, USB video capture card, notebook and RS232 transmission. Those equipments are enough to make a set of automatic face gender recognition system to be used in surveillance. This system can use interface programming to control a lot of functions of PTZ camera via RS232 transmission line, including power, rotating, scale and Backlight compensation.

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