期刊論文
| 學年 | 104 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2015-12-01 |
| 作品名稱 | A fast face detection method for illumination variant condition |
| 作品名稱（其他語言） | |
| 著者 | C.-H. Hsia; J.-S. Chiang; C.-Y. Lin |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Scientia Iranica B 22(6), pp.2081-2091 |
| 摘要 | General boosting algorithms for face detection use rectangular features. To obtain a better performance, it needs more training samples and may generate an unpredictable number of features. Besides using pixel values, which are easily affected by illumination, to calculate the rectangular features, it usually needs to preprocess the data before calculating the values of the features. Such an approach may increase computation time. To overcome the drawbacks, we propose a new solution based on the Adaboost algorithm and the Back Propagation Network (BPN) of a Neural Network (NN), combining local and global features with cascade architecture to detect human faces. We use the Modified Census Transform (MCT) feature, which belongs to texture features and is less sensitive to illumination, for local feature calculation. In this approach, it is not necessary to preprocess each sub-window of the image. For classification, we use the structure of the hierarchical feature to control the number of features. With only MCT, it is easy to misjudge faces and, therefore, in this work, we include the brightness information of global features to eliminate the False Positive (FP) regions. As a result, the proposed approach can have a Detection Rate (DR) of 99%, an FPs of only 11, and detection speed of 27.92 Frames Per Second (FPS). |
| 關鍵字 | Illumination variant face detection;Adaboost;Neural network;Modi_ed census transform;Real-time detection |
| 語言 | en |
| ISSN | 1026-3098 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | Jen-Shiun Chiang |
| 審稿制度 | 是 |
| 國別 | IRN |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/108295 ) |