Human face age estimation method based on fusion of deep characteristics and shallow characteristics

Abstract

The invention discloses a human face age estimation method based on the fusion of deep characteristics and shallow characteristics. The method comprises the following steps that: preprocessing each human face sample image in a human face sample dataset; training a constructed initial convolutional neural network, and selecting a convolutional neural network used for human face recognition; utilizing a human face dataset with an age tag value to carry out fine tuning processing on the selected convolutional neural network, and obtaining a plurality of convolutional neural networks used for age estimation; carrying out extraction to obtain multi-level age characteristics corresponding to the human face, and outputting the multi-level age characteristics as the deep characteristics; extracting the HOG (Histogram of Oriented Gradient) characteristic and the LBP (Local Binary Pattern) characteristic of the shallow characteristics of each human face image; constructing a deep belief network to carry out fusion on the deep characteristics and the shallow characteristics; and according to the fused characteristics in the deep belief network, carrying out the age regression estimation of the human face image to obtain an output an age estimation result. By sue of the method, age estimation accuracy is improved, and the method owns a human face image age estimation capability with high accuracy.

Description

technical field [0001] The invention relates to a face age estimation method based on fusion of deep features and shallow features, belonging to the technical field of image processing technology. Background technique [0002] With the development of pattern recognition, face recognition has also become a hot spot. Many emerging technologies also rely on facial recognition. Among them, face age estimation, as one of the branches, has received extensive attention because of its potential applications in identity authentication, human-computer interface, video retrieval, and robot vision. [0003] Internationally, Young and Niels were the first to propose age estimates. They proposed age estimation through face images as early as 1994. But their work is relatively simple. They divide ages roughly into three categories: children, young adults, and older adults. Hayashi et al. studied age and gender recognition methods based on wrinkle texture and skin color analysis of hum...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a face age estimation method based on the fusion of deep features and shallow features, which solves the problem that it is difficult to use a unified model to define the face in the existing estimation methods The age of the image, the specific age cannot be accurately estimated for the feature extraction and recognition of the face image, and the age estimation under the neural network cannot be realized

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