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

A neural network and convolutional neural network technology, applied in the field of face point detection system based on multi-task regularization and layer-by-layer supervised neural network, can solve the problems of over-fitting and feature robustness uncertainty, and achieve enhanced The overall generalization ability, the effect of reducing gradient dispersion and enhancing transparency

CN105469041AActive Publication Date: 2016-04-06SHANGHAI JIAO TONG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
Publication Date
2016-04-06

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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.
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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...

Examples

Embodiment Construction

[0026] The present invention will be described in detail below in conjunction with specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0027] Aiming at the problems existing in the traditional convolutional neural network, the present invention proposes a human face point detection system based on multi-task regularization and layer-by-layer supervisory neural network. In the multi-task regularization part, this system aims at the over-fitting problem of traditional convolutional neural networks, and uses the advantages of related task labels to learn the common feature representation of high-level recognition tasks. In the layer...