Face recognition algorithm configuration based on unbalance tag information fusion

Abstract

The present invention discloses a face recognition algorithm configuration based on unbalance tag information fusion. The face recognition algorithm configuration comprises two layers of configurations (L1 and L2); the L1 is configured to train the face data and corresponding tag information to obtain an initial face recognition model 1 through adoption of a supervised learning algorithm, train no-tag data through adoption of an unsupervised method, alternately optimize the face data tag information and the parameters of the model 1, and obtaining a final face recognition model 1 through calculation after multiple iterations; the L2 has a thought opposite to the L1, the L2 is configured to initiate parameters of a face recognition model 2 at random, then perform unsupervised training to update the parameters of the model 2, input tag data, continuing training through adoption of the supervised learning algorithm, and finally obtaining the face recognition model 2; and the model 1 and the model 2 are fused to obtain a final face recognition model. Through combination of the advantages of a supervised learning algorithm and an unsupervised learning algorithm, the face recognition algorithm configuration based on unbalance tag information fusion gives full play to mass of no-tag data to allow the algorithm to have excellent recognition capability in a special scene and adapt different scenes.

Description

technical field [0001] The invention relates to the field of computer biometric feature recognition, in particular to a face recognition algorithm architecture based on unbalanced label information fusion. Background technique [0002] With the gradual maturity of deep learning theory and the massive increase of labeled face data, more and more face recognition algorithms choose to use deep learning, which has greatly improved the performance of face recognition algorithms in recent years. However, such methods face two problems: (1) Most face recognition algorithms based on deep learning require large-scale labeled data, and also require data to be diverse, such as containing multiple races, different poses, multiple sources and different lighting etc. How to obtain face data with label information has become a bottleneck in improving face recognition performance. With the extensive use of sensors, a large amount of data is generated every day, and the calibration of thes...

AI Technical Summary

Problems solved by technology

However, such methods face two problems: (1) Most face recognition algorithms based on deep learning require large-scale labeled data, and also require data to be diverse, such as containing multiple races, different poses, multiple sources and different Lighting, etc.

With the extensive use of sensors, a large amount of data will be generated every day, and the calibration of these data will consume a lot of manpower and material resources, and the cost will be huge, resulting in a large amount of unlabeled data that cannot be fully utilized in practical applications.

(2) Under the conditions of existing data scale and computing resources, because the scale and diversity of data used for training are not up to the requirements, over-learning often occurs, resulting in poor generalization ability of the obtained model. Can achieve good performance in some scenarios, once the scene is switched, the performance will drop significantly

However, it is precisely because there is no label information constraint that unsupervised learning methods generally have the problem of poor pertinence, resulting in poor performance in practical applications, so this type of method is still mainly in the research stage.

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