Evaluation method and device for face image similarity

Abstract

The invention discloses an evaluation method and device for face image similarity, and the method comprises the steps: collecting face image information, obtaining a face multi-dimensional feature vector, and carrying out the In < t > quantification of the face multi-dimensional feature vector; carrying out dimensionality reduction on the quantized face multi-dimensional feature vectors, and taking d-dimensional feature values with high contribution degrees; training face image data, counting feature value distribution of each dimension and segmenting to divide a value domain of the feature value of each dimension into k sections with unequal widths; for the two face image feature vectors of which the similarity is to be recognized, obtaining the similarity of each dimension according to the segment where the feature value of each dimension is located, and obtaining an evaluation value of the similarity of the face image vectors according to the similarity of each dimension. Accordingto the embodiment of the invention, the method can reduce the consumption of system resources, achieves the quick and objective calculation of the similarity of the feature vectors of different humanface images, improves the efficiency and applicability of a clustering algorithm, and can achieve the quick and accurate judgment of the similarity of the feature vectors of the human face.

Description

technical field [0001] The present application relates to image processing, in particular to a method and device for evaluating the similarity of human face images. Background technique [0002] With the development of deep learning and big data technology, the application of face recognition technology is constantly expanding and deepening, and the scale of face image data generated every day is constantly expanding. The face image clustering algorithm aims to cluster similar image data into a cluster based on the similarity evaluation standard of face image features. The ideal state is to cluster all images of the same person into one class. In addition to saving storage space and speeding up face comparison and retrieval, this technology plays an important role in face image feature fusion, target deployment and control, pedestrian space-time trajectory analysis, suspect alarm, tracking and other fields. [0003] The clustering algorithm pursues that the distance within ...

AI Technical Summary

Problems solved by technology

For example, the Euclidean distance will cause the distance from each sample point to the center point to increase as the dimension increases due to the "empty space" phenomenon, and the difference between the minimum and maximum distances will continue to decrease, which eventually leads to the Euclidean distance as a measure of similarity. Failed, affecting the accuracy

In addition, the use of Euclidean distance or cosine distance in conventional clustering means that high-dimensional features (more than 500 dimensions) need to be multiplied, which will generate a huge amount of calculation, which not only consumes system resources, but also reduces efficiency.

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