Abstract: The present invention relates to a delivery system for nucleic acid using a cationic polymer conjugate, and more specifically relates to a delivery system for nucleic acid comprising a cationic polymer conjugate prepared by conjugating hyaluronic acid or its derivative and polyethyleneimine, and a composition of delivering a nucleic acid into mammalian cell comprising a complex of the nucleic acid and a cationic polymer conjugate with electrostatic binding.

Abstract: The present invention relates to a delivery system for nucleic acid using a cationic polymer conjugate, and more specifically relates to a delivery system for nucleic acid comprising a cationic polymer conjugate prepared by conjugating hyaluronic acid or its derivative and polyethyleneimine, and a composition of delivering a nucleic acid into mammalian cell comprising a complex of the nucleic acid and a cationic polymer conjugate with electrostatic binding.

Abstract: A statistical facial feature extraction method is disclosed. In a training phase, N training face images are respectively labeled n feature points located in n different blocks to form N feature vectors. Next, a principal component analysis (PCA) technique is used to obtain a statistical face shape model after aligning each shape vector with a reference shape vector. In an executing phase, initial positions for desired facial features are firstly guessed according to the coordinates of the mean shape for aligned training face images obtained in the training phase, and k candidates are respectively labeled in n search ranges corresponding to above-mentioned initial positions to obtain kn different combinations of test shape vectors. Finally, coordinates of the test shape vector having the best similarity with the mean shape for aligned training face image and the statistical face shape model are assigned as facial features of the test face image.

Abstract: A statistical facial feature extraction method is disclosed. In a training phase, N training face images are respectively labeled n feature points located in n different blocks to form N feature vectors. Next, a principal component analysis (PCA) technique is used to obtain a statistical face shape model after aligning each shape vector with a reference shape vector. In an executing phase, initial positions for desired facial features are firstly guessed according to the coordinates of the mean shape for aligned training face images obtained in the training phase, and k candidates are respectively labeled in n search ranges corresponding to above-mentioned initial positions to obtain kn different combinations of test shape vectors. Finally, coordinates of the test shape vector having the best similarity with the mean shape for aligned training face image and the statistical face shape model are assigned as facial features of the test face image.