Quick retrieval method of massive face images based on global feature and local feature fusion
Wei Yu, Qiuyu Zhu · 2017
In order to retrieve the image quickly and accurately in the massive image library, this paper proposes a fast retrieval method for massive face images based on global feature and local feature. Firstly, we use the local binary model feature (LBP) to extract the local face features, such as the tip of the nose, the mouth, and eye pupil etc. Then, the image global features are extracted and are integrated with the local features as our retrieval features. The principal component analysis (PCA) is used to reduce the dimensions of the features to 64, and the reduced dimension is encoded to generate an image signature, whose inverted index table is constructed for the image library and used for efficient retrieval. By testing on the 110,000 experimental datasets, the method can accurately retrieve the desired image within 0.3s using single-thread program.