Face Model from Local Features: Image Clustering and Common Local Feature Extraction based on Diverse Density

• Item type: SIG Technical Reports(1)
• 公開日: 2013-05-23
• タイトル: Face Model from Local Features: Image Clustering and Common Local Feature Extraction based on Diverse Density
• タイトル / 言語: en
• タイトル: Face Model from Local Features: Image Clustering and Common Local Feature Extraction based on Diverse Density
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: 卒論ショートアピール
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Wakayama University
• 著者所属: Wakayama University
• 著者所属: Wakayama University
• 著者所属(英): en
• 著者所属(英): Wakayama University
• 著者所属(英): en
• 著者所属(英): Wakayama University
• 著者所属(英): en
• 著者所属(英): Wakayama University
• 著者名: Takayuki, Fukui; Toshikazu, Wada; Hiroshi, Oike
• 著者名(英): Takayuki, Fukui; Toshikazu, Wada; Hiroshi, Oike
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Face image retrieval based on local features has advantages of short elapsed time and robustness against the occlusions. However, the keypoint detection, beforehand with the feature description, may fail due to illumination changes. For solving this problem, top-down model-based keypoint detection can be applied, where man-made face model does not fit this task. This report addresses the problem of bottom-up face model construction from example, which can be formalized as common local features extraction among face images. For this purpose, a measure called Diverse Density (DD) can be applied. DD at a point in a feature space represents how the point is close to other positive example while keeping enough distance from negative examples. Because of this property, DD is defined as product of metrics, which can easily be affected by exceptional data, i.e., if one negative data leaps into the neighbour of a positive example, the DD around there becomes lower. Actually, face images have wide variations of face organs' positions, beard, moustache, glasses, and so on. Under these variations, DD for wide varieties of face images will be low at any point in the feature space. For solving this problem, we propose a method performing hierarchical clustering and common local feature extraction simultaneously. In this method, we define a measure representing the affinity of two face image sets, and cluster the face images by iteratively merging the cluster pair having the maximum score. Through experiments on 1021 CAS-PEAL face images, we confirmed that multiple face models are successfully constructed.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Face image retrieval based on local features has advantages of short elapsed time and robustness against the occlusions. However, the keypoint detection, beforehand with the feature description, may fail due to illumination changes. For solving this problem, top-down model-based keypoint detection can be applied, where man-made face model does not fit this task. This report addresses the problem of bottom-up face model construction from example, which can be formalized as common local features extraction among face images. For this purpose, a measure called Diverse Density (DD) can be applied. DD at a point in a feature space represents how the point is close to other positive example while keeping enough distance from negative examples. Because of this property, DD is defined as product of metrics, which can easily be affected by exceptional data, i.e., if one negative data leaps into the neighbour of a positive example, the DD around there becomes lower. Actually, face images have wide variations of face organs' positions, beard, moustache, glasses, and so on. Under these variations, DD for wide varieties of face images will be low at any point in the feature space. For solving this problem, we propose a method performing hierarchical clustering and common local feature extraction simultaneously. In this method, we define a measure representing the affinity of two face image sets, and cluster the face images by iteratively merging the cluster pair having the maximum score. Through experiments on 1021 CAS-PEAL face images, we confirmed that multiple face models are successfully constructed.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11131797
• 書誌情報: 研究報告コンピュータビジョンとイメージメディア（CVIM）; 巻 2013-CVIM-187, 号 33, p. 1-5, 発行日 2013-05-23
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会