CN111144310A - Face detection method and system based on multi-layer information fusion - Google Patents
Face detection method and system based on multi-layer information fusion Download PDFInfo
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Abstract
The invention discloses a face detection method based on multilayer information fusion, which relates to the technical field of face detection and comprises the following steps: extracting the features of a face image to be detected according to a pre-generated neural network model to obtain an initial feature map; performing a first convolution operation on the initial characteristic diagram to obtain a first characteristic diagram; performing second convolution operation on the initial characteristic diagram to obtain a second characteristic diagram; respectively performing third convolution operation and fourth convolution operation on the second feature map to obtain a corresponding third feature map and a corresponding fourth feature map; performing a fifth convolution operation on the third feature map to obtain a fifth feature map; performing first up-sampling on the fifth feature map, and performing feature fusion on the first up-sampling result and the fourth feature map to obtain a sixth feature map; and performing second up-sampling on the sixth feature map, performing feature fusion on the second up-sampling result and the first feature map to obtain a seventh feature map, and performing face detection according to the seventh feature map. The invention effectively improves the accuracy of face detection.
Description
Technical Field
The invention relates to the technical field of face detection, in particular to a face detection method and system based on multilayer information fusion.
Background
The human face detection belongs to a basic research direction in the field of computer vision, the human face detection is applied to daily life of people, most of the current mainstream human face detection methods are based on deep learning, and the main process is to extract image characteristics through a deep learning network aiming at an input image and judge whether a human face and a human face position exist according to the characteristics.
With the development of CNN, most face detection is currently based on convolutional neural network, and a quite good effect is achieved. In the process of face detection, the convolutional neural network has the characteristics of a high-level convolutional layer rich in abundant semantic information and a low-level convolutional layer rich in abundant detail information.
Disclosure of Invention
The invention aims to provide a face detection method and system based on multi-layer information fusion.
In order to achieve the purpose, the invention adopts the following technical scheme:
the method for detecting the human face based on the multi-layer information fusion specifically comprises the following steps:
step S1, extracting the features of the face image to be detected according to a pre-generated neural network model to obtain an initial feature map;
step S2, performing a first convolution operation on the initial characteristic diagram to obtain a first characteristic diagram;
step S3, performing a second convolution operation on the initial characteristic diagram to obtain a second characteristic diagram;
step S4, performing a third convolution operation and a fourth convolution operation on the second feature map respectively to obtain a corresponding third feature map and a corresponding fourth feature map;
step S5, performing a fifth convolution operation on the third feature map to obtain a fifth feature map;
step S6, performing first upsampling on the fifth feature map, and performing feature fusion on the first upsampling result and the fourth feature map to obtain a sixth feature map;
and step S7, performing second upsampling on the sixth feature map, performing feature fusion on a second upsampling result and the first feature map to obtain a seventh feature map, and performing face detection according to the seventh feature map.
As a preferred aspect of the present invention, the convolution kernel size of the first convolution operation is 3 × 3.
As a preferred aspect of the present invention, the convolution kernel size of the second convolution operation is 3 × 3, and the step size is 2.
As a preferred aspect of the present invention, the convolution kernel size of the third convolution operation is 3 × 3, and the step size is 2.
As a preferred aspect of the present invention, the convolution kernel size of the fourth convolution operation is 3 × 3.
As a preferred aspect of the present invention, the convolution kernel size of the fifth convolution operation is 1 × 1.
As a preferable aspect of the present invention, the first upsampling is 2 times upsampling.
As a preferable aspect of the present invention, the second upsampling is 2 times upsampling.
A face detection system based on multilayer information fusion applies any one of the above face detection methods based on multilayer information fusion, and specifically comprises:
the characteristic extraction module is used for extracting the characteristics of the face image to be detected according to a pre-generated neural network model to obtain an initial characteristic diagram;
the first convolution module is connected with the feature extraction module and used for performing first convolution operation on the initial feature map to obtain a first feature map;
the second convolution module is connected with the feature extraction module and used for performing second convolution operation on the initial feature map to obtain a second feature map;
the third convolution module is connected with the second convolution module and is used for respectively performing third convolution operation and fourth convolution operation on the second feature map to obtain a corresponding third feature map and a corresponding fourth feature map;
the fourth convolution module is connected with the third convolution module and used for performing fifth convolution operation on the third feature map to obtain a fifth feature map;
the fifth convolution module is connected with the fourth convolution module and used for performing first up-sampling on the fifth feature map and performing feature fusion on the first up-sampling result and the fourth feature map to obtain a sixth feature map;
and the sixth convolution module is connected with the fifth convolution module and used for performing second up-sampling on the sixth feature map, performing feature fusion on a second up-sampling result and the first feature map to obtain a seventh feature map, and performing face detection according to the seventh feature map.
The invention has the beneficial effects that: the method comprises the steps of considering the relation among the features of different convolutional layers, coupling the features of the different convolutional layers together, generating new improved features, and utilizing the improved features to detect the face, so that the accuracy of the face detection is effectively improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below. It is obvious that the drawings described below are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 is a schematic flow chart of a face detection method based on multi-layer information fusion according to an embodiment of the present invention.
Fig. 2 is a schematic diagram illustrating a principle of a face detection method based on multi-layer information fusion according to an embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a face detection system based on multi-layer information fusion according to an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further explained by the specific implementation mode in combination with the attached drawings.
Wherein the showings are for the purpose of illustration only and are shown by way of illustration only and not in actual form, and are not to be construed as limiting the present patent; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "inner", "outer", etc. are used for indicating the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not indicated or implied that the referred device or element must have a specific orientation, be constructed in a specific orientation and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and are not to be construed as limitations of the present patent, and the specific meanings of the terms may be understood by those skilled in the art according to specific situations.
In the description of the present invention, unless otherwise explicitly specified or limited, the term "connected" or the like, if appearing to indicate a connection relationship between the components, is to be understood broadly, for example, as being fixed or detachable or integral; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or may be connected through one or more other components or may be in an interactive relationship with one another. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Based on the technical problems in the prior art, the invention provides a face detection method based on multi-layer information fusion, as shown in fig. 1, which specifically comprises the following steps:
step S1, extracting the features of the face image to be detected according to a pre-generated neural network model to obtain an initial feature map;
step S2, performing a first convolution operation on the initial characteristic diagram to obtain a first characteristic diagram;
step S3, performing a second convolution operation on the initial characteristic diagram to obtain a second characteristic diagram;
step S4, performing a third convolution operation and a fourth convolution operation on the second feature map respectively to obtain a corresponding third feature map and a corresponding fourth feature map;
step S5, performing a fifth convolution operation on the third feature map to obtain a fifth feature map;
step S6, performing first upsampling on the fifth feature map, and performing feature fusion on the first upsampling result and the fourth feature map to obtain a sixth feature map;
and step S7, performing second upsampling on the sixth feature map, performing feature fusion on the second upsampling result and the first feature map to obtain a seventh feature map, and performing face detection according to the seventh feature map.
Specifically, in this embodiment, when performing face detection, the face detection method based on multi-layer information fusion of the present invention improves the existing features to perform face detection by associating the relationship between the high-level features and the low-level features, which is helpful to improve the performance of the current face detector.
Further, as shown in fig. 2, the face detection and accuracy are effectively improved by further feature optimization of the initial feature map. The feature optimization specifically includes obtaining two higher layers of information by Conv (convolution) with two convolution kernels having a size of 3 × 3 and a step size (stride) of 2, unifying the 3 different layers of information into the same value domain by 3x3Conv (convolution with a convolution kernel size of 3 × 3), 1x1Conv (convolution with a convolution kernel size of 1 × 1), and finally fusing the two different layers of information together by 2x up (2 times up sampling) and sum (feature fusion).
As a preferred aspect of the present invention, the convolution kernel size of the first convolution operation is 3 × 3.
As a preferred aspect of the present invention, the convolution kernel size of the second convolution operation is 3 × 3 and the step size is 2.
As a preferred aspect of the present invention, the convolution kernel size of the third convolution operation is 3 × 3 and the step size is 2.
As a preferred aspect of the present invention, the convolution kernel size of the fourth convolution operation is 3 × 3.
As a preferred aspect of the present invention, the convolution kernel size of the fifth convolution operation is 1 × 1.
As a preferable aspect of the present invention, the first upsampling is 2 times upsampling.
As a preferred aspect of the present invention, the second upsampling is 2 times upsampling.
A face detection system based on multi-layer information fusion, which applies any one of the above face detection methods based on multi-layer information fusion, as shown in fig. 3, specifically includes:
the characteristic extraction module 1 is used for extracting the characteristics of a face image to be detected according to a pre-generated neural network model to obtain an initial characteristic diagram;
the first convolution module 2 is connected with the feature extraction module 1 and is used for performing first convolution operation on the initial feature map to obtain a first feature map;
the second convolution module 3 is connected with the feature extraction module 1 and is used for performing second convolution operation on the initial feature map to obtain a second feature map;
the third convolution module 4 is connected with the second convolution module 3 and is used for respectively performing third convolution operation and fourth convolution operation on the second feature map to obtain a corresponding third feature map and a corresponding fourth feature map;
the fourth convolution module 5 is connected with the third convolution module 4 and is used for performing fifth convolution operation on the third feature map to obtain a fifth feature map;
the fifth convolution module 6 is connected with the fourth convolution module 5 and is used for performing first up-sampling on the fifth feature map and performing feature fusion on the first up-sampling result and the fourth feature map to obtain a sixth feature map;
and the sixth convolution module 7 is connected with the fifth convolution module 6 and is used for performing second upsampling on the sixth feature map, performing feature fusion on the second upsampling result and the first feature map to obtain a seventh feature map, and performing face detection according to the seventh feature map.
It should be understood that the above-described embodiments are merely preferred embodiments of the invention and the technical principles applied thereto. It will be understood by those skilled in the art that various modifications, equivalents, changes, and the like can be made to the present invention. However, such variations are within the scope of the invention as long as they do not depart from the spirit of the invention. In addition, certain terms used in the specification and claims of the present application are not limiting, but are used merely for convenience of description.
Claims (9)
1. A face detection method based on multi-layer information fusion is characterized by comprising the following steps:
step S1, extracting the features of the face image to be detected according to a pre-generated neural network model to obtain an initial feature map;
step S2, performing a first convolution operation on the initial characteristic diagram to obtain a first characteristic diagram;
step S3, performing a second convolution operation on the initial characteristic diagram to obtain a second characteristic diagram;
step S4, performing a third convolution operation and a fourth convolution operation on the second feature map respectively to obtain a corresponding third feature map and a corresponding fourth feature map;
step S5, performing a fifth convolution operation on the third feature map to obtain a fifth feature map;
step S6, performing first upsampling on the fifth feature map, and performing feature fusion on the first upsampling result and the fourth feature map to obtain a sixth feature map;
and step S7, performing second upsampling on the sixth feature map, performing feature fusion on a second upsampling result and the first feature map to obtain a seventh feature map, and performing face detection according to the seventh feature map.
2. The method for detecting the human face based on the multi-layer information fusion of claim 1, wherein the convolution kernel size of the first convolution operation is 3x 3.
3. The method according to claim 1, wherein the convolution kernel size of the second convolution operation is 3x3 and the step size is 2.
4. The method according to claim 1, wherein the convolution kernel size of the third convolution operation is 3x3 and the step size is 2.
5. The method for detecting a human face based on multi-layer information fusion according to claim 1, wherein the convolution kernel size of the fourth convolution operation is 3x 3.
6. The method for detecting a human face based on multi-layer information fusion according to claim 1, wherein the convolution kernel size of the fifth convolution operation is 1x 1.
7. The method for detecting human face based on multi-layer information fusion of claim 1, wherein the first upsampling is 2 times upsampling.
8. The method for detecting human face based on multi-layer information fusion of claim 1, wherein the second upsampling is 2 times upsampling.
9. A face detection system based on multi-layer information fusion, which is characterized by applying the face detection method based on multi-layer information fusion according to any one of claims 1 to 8, and specifically comprises:
the characteristic extraction module is used for extracting the characteristics of the face image to be detected according to a pre-generated neural network model to obtain an initial characteristic diagram;
the first convolution module is connected with the feature extraction module and used for performing first convolution operation on the initial feature map to obtain a first feature map;
the second convolution module is connected with the feature extraction module and used for performing second convolution operation on the initial feature map to obtain a second feature map;
the third convolution module is connected with the second convolution module and is used for respectively performing third convolution operation and fourth convolution operation on the second feature map to obtain a corresponding third feature map and a corresponding fourth feature map;
the fourth convolution module is connected with the third convolution module and used for performing fifth convolution operation on the third feature map to obtain a fifth feature map;
the fifth convolution module is connected with the fourth convolution module and used for performing first up-sampling on the fifth feature map and performing feature fusion on the first up-sampling result and the fourth feature map to obtain a sixth feature map;
and the sixth convolution module is connected with the fifth convolution module and used for performing second up-sampling on the sixth feature map, performing feature fusion on a second up-sampling result and the first feature map to obtain a seventh feature map, and performing face detection according to the seventh feature map.
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