Facial point detection system based on multi-task regularization and layer-by-layer supervision neural networ

Abstract

The invention discloses a facial point detection system based on multi-task regularization and a layer-by-layer supervision neural network. The system comprises a multi-task regularization module and a layer-by-layer supervision network module. The multi-task regularization module includes a main task and a related task; and the main task and the related task study jointly to obtain a common feature space and then an additional regular term is provided by using an auxiliary tag of the related task to enhance a generalization ability of a network. The layer-by-layer supervision network module, different from the traditional convolution neural network only optimizing an objective function of an output layer, introduces a supervision objective function into each interlayer, thereby enhancing the saliency of features obtained by studying of the interlayers. Therefore, problems that overfitting occurs and the feature robustness is uncertain according to the traditional convolution neural network can be solved effectively.

Description

technical field [0001] The invention relates to a human face point detection method in the field of computer vision, in particular to a human face point detection system based on multi-task regularization and layer-by-layer supervision neural network. Background technique [0002] In the field of computer vision, the detection of face points, such as eyes, nose, mouth, etc., is a very basic and important issue, which is the basis for subsequent face recognition, tracking and 3D face modeling. Even with a lot of research invested in it, face point detection is still a challenging problem in limited environments due to the varying head poses and partial occlusions in images. [0003] The existing face point detection methods are mainly divided into two categories: template adaptation and regression-based methods. The regression-based method first performs feature extraction on the input image, and then maps the learned features to the space of facial feature points. The conv...

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Problems solved by technology

[0006] Aiming at the defects in the prior art, the present invention provides a face point detection system based on multi-task regularization and layer-by-layer supervision neural network, which can effectively solve the over-fitting and feature robustness of the traditional convolutional neural network. identified problem

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