Human face identification method and apparatus, computer equipment and readable storage medium

Abstract

The invention provides a human face identification method and apparatus, computer equipment and a readable storage medium. The method comprises the following steps: any human face image in a trainingset is input into a depth residual error network module group for training operation, a human force identification model is built; human face identification characteristics with specified dimensions are output, corresponding categories of multiple tasks for a corresponding human face image are predicted according to the human face identification characteristics with the specified dimensions basedon a full link layer classifier; a training loss value is calculated according to the predicted corresponding categories of multiple tasks for the corresponding human face image based on a loss function; whether to input any human face image in the training set into the depth residual error network module group for the training operation is determined according to the training loss value, and thehuman force identification model is built. Via the human face identification method and apparatus, the computer equipment and the readable storage medium disclosed in a technical solution of the invention, in-depth of training and construction of the human face identification model can be realized; multiple tasks can be subjected to the training operation, the human face identification model obtained based on the training operation is high in identification effects, multiple tasks of human face identification can be performed, and identification efficiency can be improved.

Description

technical field [0001] The present invention relates to the technical field of face recognition, in particular, to a face recognition method based on a multi-task deep residual network, a face recognition device based on a multi-task deep residual network, a computer device and a A computer readable storage medium. Background technique [0002] In related technologies, generally by designing a fixed feature extraction method, such as an active appearance model, a face measurement model, etc., and then removing redundant or irrelevant features through a feature selection algorithm, a feature subset is obtained for matching and recognition, or by Neural network machine learning is used to extract and train features based on a single task to obtain a recognition model, and then identify a single task based on the recognition model, which has the following technical defects: [0003] (1) By designing a fixed feature extraction method, it relies heavily on the experience of huma...

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Problems solved by technology

[0003] (1) By designing a fixed feature extraction method, it relies heavily on the experience of human experts, and it needs repeated experiments to complete, the workload is large, and it is difficult to find a facial feature expression model that integrates multiple tasks, and the face recognition effect is poor. Difference

[0004] (2) Through the neural network machine learning of a single task, when performing multi-task complete face recognition, it is necessary to run multiple networks, which is inefficient, and the depth of neural network machine learning is shallow, and the recognition effect is poor

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