Method for building deep learning based face recognition and age synthesis joint model

A face recognition and deep learning technology, applied in the field of computer vision, can solve problems such as the direction of face recognition to be explored, and achieve the effect of flexibility

Active Publication Date: 2017-05-10
SYSU CMU SHUNDE INT JOINT RES INST +1
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Problems solved by technology

[0008] Although the above methods have made great progress, the direction of face recognition across ages has yet to be explored.

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  • Method for building deep learning based face recognition and age synthesis joint model

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Embodiment 1

[0046] Such as figure 1 As shown, a method for building a joint model of face recognition and age synthesis based on deep learning includes the following steps:

[0047] S1: Slice preprocessing of the image: align according to the center of the eyes, use PCA and LDA to reduce dimensionality, and achieve the purpose of increasing the gap between classes;

[0048] S2: Encoding: An autoencoder obtained from the training data encodes the input feature vector. The purpose of the encoder is to synthesize new features from the original image features through a certain encoding method to express identity or age-related information. For any input image, the encoder will generate six different expressions:

[0049] The first group is identity expression, which is the mapping encoding of the original feature minus the average face, reflecting the stable information of the individual's identity;

[0050] The second group to the sixth group are the expression of the composite image of th...

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Abstract

The invention provides a method for building a deep learning based face recognition and age synthesis joint model. The method is characterized in that alignment and PCA and LDA dimension reduction preprocessing are performed on a pair of input images; six groups of features for identity representation and different age group representation are acquired through an automatic encoder acquired by training, an image similarity degree is outputted for each of the six results through a parallel CNN, then a matching result is acquired through weighted fusion. The method provided by the invention has an excellent effect for independent face recognition or age detection or a common task, and can also acquire an excellent effect for face recognition under the influence of illumination and postures; and the method also has robustness for cross-age face recognition because features of the age and the face identity are separated. In addition, some parameters and weights can be adjusted according to requirements, so that the method is very flexible.

Description

technical field [0001] The present invention relates to the field of computer vision, and more specifically, to a method for constructing a joint model of face recognition and age synthesis based on deep learning. Background technique [0002] Face recognition is a biometric technology for identification based on human facial feature information. A series of related technologies that use a video camera or camera to collect images or video streams containing human faces, automatically detect and track human faces in the images, and then perform facial recognition on the detected faces, usually also called portrait recognition and facial recognition. [0003] Since face recognition classification and verification have great practical application value, this topic has been a research hotspot for many years. Face recognition has a wide application prospect in real life. For example, face recognition is required in security access control systems, public security criminal inves...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/178G06V40/172G06F18/2415
Inventor 胡海峰杜灵双李昊曦
Owner SYSU CMU SHUNDE INT JOINT RES INST
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