Model training method, relative relationship identification method, electronic equipment and storage medium

Abstract

The embodiment of the invention relates to the field of face recognition, and discloses a model training method, a relative relationship recognition method, electronic equipment and a storage medium. The model training method comprises the following steps: constructing a family face feature extraction model for extracting family face features from family image samples; wherein the family image sample comprises a plurality of face images belonging to the same family, and the family face feature extraction model is provided with organ parameters for fusing individual face features of the plurality of face images to form family face features; the organ parameter represents the dominant degree of the face feature of the corresponding organ region in the individual face feature in the family face feature; and constructing a loss function according to the distance between the family face feature of each family and the corresponding family label and the distance between the family face feature and the face feature of each face image in the corresponding family image sample, and training a family face feature extraction model.

Description

technical field [0001] The embodiments of the present application relate to the field of face recognition, and in particular to a model training method, a kinship recognition method, electronic equipment, and a storage medium. Background technique [0002] Kinship recognition based on face images has been widely used in paternity testing, missing children search and other fields. At present, the technical solution commonly used in kinship recognition is the kinship recognition based on the convolutional neural network model. This method inputs multiple training atlases into the convolutional neural network, and trains through the binary classification loss function to obtain the kinship recognition. Model. [0003] However, this method only relies on the training of the binary classification loss function, and the supervision signal in the training process is weak, resulting in low recognition accuracy. Contents of the invention [0004] The purpose of the embodiments of...

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Problems solved by technology

[0003] However, this method only relies on the training of the binary classification loss function, and the supervision signal in the training process is weak, resulting in low recognition accuracy.

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