Method and device for creating sample library for large-scale face mode analysis

Abstract

The invention discloses a method and a device for creating a sample library for large-scale face mode analysis. The method comprises the following steps: obtaining pictures in batches through a network; performing face detection and screening on the obtained pictures by adopting a Viola-Jones face detection algorithm; performing normalization processing on the pictures with faces based on a face ratio; performing gender classification on cut pictures by adopting PCA (principal component analysis) and LDA (linear discriminant analysis) algorithms; comparing similarity of grey histograms of the pictures to remove repeated pictures; performing secondary screening and artificial marking on the face sample library as needed and storing the face sample library into a local memory. The device comprises a network communication interface, display equipment, input equipment, a memory and a processor, wherein the processor is used for executing the method for creating the sample library for large-scale face mode analysis. The method and the device reduce the labor intensity of scientific research personnel during creation of the face sample library used for face mode analysis to a relatively great extent, increase the creation speed of the sample library, and also can be used for creating sample libraries for related scientific research of other mode identification.

Description

technical field [0001] The invention relates to a method and device for constructing a large-scale human face pattern analysis sample library, and belongs to the technical field of computer image processing and pattern recognition. Background technique [0002] In the context of big data and artificial intelligence, how to acquire, store and process large-scale data is a very challenging problem. In the process of studying intelligent algorithms and engineering practice, experimental samples are an important tool for training and evaluating intelligent algorithms, and the scale of experimental samples is even more important than the exquisite design of algorithm models. At present, researchers and engineers in related fields generally build face databases manually. The process is time-consuming and labor-intensive, which severely limits the scale and expansion of the sample database. [0003] Today's sample libraries for face pattern analysis are generally small in size. O...

AI Technical Summary

Problems solved by technology

On the one hand, the problem caused by a sample library that is too small is that the algorithm cannot be fully trained and the experimental results are not objective and reliable enough.

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