Facial sample cleaning method and system based on deep learning features

Abstract

The invention is applicable to the field of data mining and sample cleaning and provides a facial sample cleaning method and system based on deep learning features. The facial sample cleaning method comprises the steps that A, the deep learning technology is utilized to extract facial image features; B, a sample needing to be processed is partitioned according to a given process number, and the partitioned samples are allocated to all processes; C, corresponding facial features in each process are clustered; D, a class with most images is selected in each clustering result to serve as a main class; E, the image corresponding to the features of the main class is copied to a target directory, and an original file structure is reserved. The facial features extracted through deep learning are low in dimension and have very good identification capability; on the one hand, sample cleaning precision can be improved, and on the other hand, cleaning time can be saved; multiple processes are designed concurrently, the operational capability of hardware an be fully utilized, therefore, the efficiency of entire operation can be improved, and consumed time is shortened.

Description

technical field [0001] The invention belongs to the field of data mining and sample cleaning. The technology used is based on image processing and machine learning, and in particular relates to a face sample cleaning method and system based on deep learning features. Background technique [0002] Extracting facial features with deep learning: Deep learning is a branch of machine learning based on a family of algorithms that attempt to perform high-level abstractions on data using multiple processing layers that contain complex structures or consist of multiple non-linear transformations. An observation (such as an image) can be represented in a variety of ways, such as a vector of intensity values ​​for each pixel, or more abstractly as a series of edges, regions of a specific shape, etc. Instead, it is easier to learn tasks from examples (e.g., face recognition or facial expression recognition) using certain representations. [0003] DBSCAN (Density-Based Spatial Clusterin...

AI Technical Summary

Problems solved by technology

[0011] Since the feature extraction method is disturbed by noise, etc., and the effect of the classification model is often closely related to the accuracy of manual tuning parameters, the final accuracy is low, and the effect of distinguishing different faces is not good.

2. Due to the high feature dimension and the use of serial operation design, the whole process is inefficient and time-consuming. As the number of images increases, the time-consuming tends to increase exponentially.

3. When building the initial model, it is often necessary to manually mark part of the data

[0015] Based on the above-mentioned shortcomings in the prior art and other reasons such as single-threading, the implementation methods of the prior art are generally less accurate and time-consuming

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