Face recognition method based on deep transformation learning in unconstrained scene

A technology of face recognition and learning methods, applied in the direction of neural learning methods, character and pattern recognition, instruments, etc., can solve problems such as performance not as expected, achieve the effect of strengthening feature transformation learning, realizing sharing, and reducing computational complexity

Active Publication Date: 2017-12-22
唐晖
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

2. Nonlinear pose and shape changes
3. Mixed variation of different types of interference
Pal et al. proposed a dense keypoint framework to extract discrimi

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  • Face recognition method based on deep transformation learning in unconstrained scene
  • Face recognition method based on deep transformation learning in unconstrained scene
  • Face recognition method based on deep transformation learning in unconstrained scene

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Embodiment Construction

[0077] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0078] In the description of the present invention, unless otherwise specified and limited, it should be noted that the terms "installation", "connection" and "connection" should be understood in a broad sense, for example, it can be mechanical connection or electrical connection, or two The internal communication of each element may be directly connected or indirectly connected through an intermediary. Those skilled in the art can understand the specific meanings of the above terms according to specific situations.

[0079] The inventio...

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Abstract

The invention discloses a face recognition method based on deep transformation learning in an unconstrained scene. The method comprises the following steps: obtaining a face image and detecting face key points; carrying out transformation on the face image through face alignment, and in the alignment process, minimizing the distance between the detected key points and predefined key points; carrying out face attitude estimation and carrying out classification on the attitude estimation results; separating multiple sample face attitudes into different classes; carrying out attitude transformation, and converting non-front face features into front face features and calculating attitude transformation loss; and updating network parameters through a deep transformation learning method until meeting threshold requirements, and then, quitting. The method proposes feature transformation in a neural network and transform features of different attitudes into a shared linear feature space; by calculating attitude loss and learning attitude center and attitude transformation, simple class change is obtained; and the method can enhance feature transformation learning and improve robustness and differentiable deep function.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a face recognition method based on depth transformation learning in an unconstrained scene. Background technique [0002] Face recognition has been intensively studied for decades and applied in many aspects due to its non-invasive nature, and now many methods have been proposed to solve the face recognition problem. However, the change of human pose cannot be controlled, and it is often difficult to obtain frontal face images in unconstrained scenes, which is one of the main reasons for the low rate of face recognition or the inaccurate recognition of face images. [0003] In constrained scenarios, such as airport check-in and cash withdrawal from ATM counters (automated teller machines), it is easy to obtain frontal face images. In fact, there are many differences between the face images detected from the unconstrained scene and the constrained scene, and the detailed...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/08
CPCG06N3/08G06V40/165G06V40/168G06V40/172G06F18/213G06F18/214
Inventor 唐晖
Owner 唐晖
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