Large-scale human face image searching method
A face image, large-scale technology, applied in the field of face image retrieval
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Embodiment 1
[0095] This embodiment includes the following parts:
[0096] 1. Location of key points of face image:
[0097] Using Active Shape Models (ASM) to locate the key points in the face image, this module is mainly divided into two steps: model training and key point search.
[0098] Principal component analysis (PCA) is used in the process of training the model.
[0099] Principal component analysis is an exploratory statistical analysis method that concentrates the information scattered on a set of variables into a few comprehensive indicators (principal components). It uses principal components to describe the internal structure of the data set, which actually plays a role in data dimensionality reduction Function, this method selects the eigenvectors corresponding to the first few largest eigenvalues of the original data covariance matrix to form a set of bases to achieve the purpose of best characterizing the original data. Consider the vector x in the n-dimensional space. In order...
Embodiment 2
[0180] Image 6 For the retrieval diagram of Example 2, the source of the image of the person in the figure is the public LFW database. In the figure, 1 is the original face image, the box in the figure represents the face block, 2 represents the extracted features, including local features and global special features, 3 represents the trained dictionary, 4 is the preliminary search result obtained from the dictionary, that is, the candidate In the face image set, 5 is the face image reference set. According to the face image reference set, the preliminary retrieval results are reordered, and 6 is the final retrieval result. The face in 6 and the face in 1 are the same person, which means the retrieval is successful.
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