Catálogo de publicaciones - libros

Face image can be seen as a complex visual object, which combines a set of characterizing facial features. These facial features are crucial hints for machine to distinguish different face images. However, the face image also contains certain amount of redundant information which can not contribute to the face image retrieval task. Therefore, in this paper we propose a retrieval system which is aim to eliminate such effect at three different levels. The Ternary Feature Vector (TFV) is generated from quantized block transform coefficients. Histograms based on TFV are formed from certain subimages. Through this way, irrelevant information is gradually removed, and the structural and statistical information are combined. We testified our ideas over the public face database FERET with the Cumulative Match Score evaluation. We show that proper selection of subimage and feature vectors can significantly improve the performance with minimized complexity. Despite of the simplicity, the proposed measures provide results which are on par with best results using other methods.

Face image can be seen as a complex visual object, which combines a set of characterizing facial features. These facial features are crucial hints for machine to distinguish different face images. However, the face image also contains certain amount of redundant information which can not contribute to the face image retrieval task. Therefore, in this paper we propose a retrieval system which is aim to eliminate such effect at three different levels. The Ternary Feature Vector (TFV) is generated from quantized block transform coefficients. Histograms based on TFV are formed from certain subimages. Through this way, irrelevant information is gradually removed, and the structural and statistical information are combined. We testified our ideas over the public face database FERET with the Cumulative Match Score evaluation. We show that proper selection of subimage and feature vectors can significantly improve the performance with minimized complexity. Despite of the simplicity, the proposed measures provide results which are on par with best results using other methods.

Face image can be seen as a complex visual object, which combines a set of characterizing facial features. These facial features are crucial hints for machine to distinguish different face images. However, the face image also contains certain amount of redundant information which can not contribute to the face image retrieval task. Therefore, in this paper we propose a retrieval system which is aim to eliminate such effect at three different levels. The Ternary Feature Vector (TFV) is generated from quantized block transform coefficients. Histograms based on TFV are formed from certain subimages. Through this way, irrelevant information is gradually removed, and the structural and statistical information are combined. We testified our ideas over the public face database FERET with the Cumulative Match Score evaluation. We show that proper selection of subimage and feature vectors can significantly improve the performance with minimized complexity. Despite of the simplicity, the proposed measures provide results which are on par with best results using other methods.

Face image can be seen as a complex visual object, which combines a set of characterizing facial features. These facial features are crucial hints for machine to distinguish different face images. However, the face image also contains certain amount of redundant information which can not contribute to the face image retrieval task. Therefore, in this paper we propose a retrieval system which is aim to eliminate such effect at three different levels. The Ternary Feature Vector (TFV) is generated from quantized block transform coefficients. Histograms based on TFV are formed from certain subimages. Through this way, irrelevant information is gradually removed, and the structural and statistical information are combined. We testified our ideas over the public face database FERET with the Cumulative Match Score evaluation. We show that proper selection of subimage and feature vectors can significantly improve the performance with minimized complexity. Despite of the simplicity, the proposed measures provide results which are on par with best results using other methods.