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A method and a system for facial landmark detection based on multi-task

A face key point and task technology, applied in the field of face alignment, can solve problems such as different convergence rates, hazard model learning convergence, and different learning difficulties, and achieve the goals of reducing complexity, reducing model complexity, and improving accuracy Effect

Active Publication Date: 2017-04-19
BEIJING SENSETIME TECH DEV CO LTD
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Problems solved by technology

[0005] However, different tasks will inherently differ in learning difficulty and have different convergence rates
Furthermore, some tasks may overfit earlier than others when learning simultaneously, which will jeopardize the learning convergence of the entire model

Method used

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  • A method and a system for facial landmark detection based on multi-task
  • A method and a system for facial landmark detection based on multi-task
  • A method and a system for facial landmark detection based on multi-task

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Embodiment Construction

[0020] This section describes exemplary embodiments in detail, examples of which are illustrated in the accompanying drawings. Where appropriate, the same reference numbers have been used throughout the drawings to refer to the same or like parts.

[0021] figure 1 is a schematic diagram illustrating an exemplary system 1000 for facial keypoint detection according to some disclosed embodiments. According to the system 1000, face landmark detection (hereinafter also referred to as the main task) is jointly optimized with at least one related / auxiliary task. Facial keypoint detection refers to detecting 2D positions, ie, 2D coordinates (x and y) of a face region of a face image. Examples of facial key points may include, but are not limited to, the left eye center and right eye center, nose, left mouth corner, and right mouth corner of the human face image. Examples of auxiliary tasks may include, but are not limited to, head pose estimation, demographics (such as gender clas...

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Abstract

The present application disclosed a method and system for detecting facial landmarks of a face image. The method may comprise extracting multiple feature maps from at least one facial region of the face image and / or the whole face image; generating a shared facial feature vector from the extracted multiple feature maps; and predicting facial landmark locations of the face image from the generated shared facial feature vector. With the present method and system, the facial landmark detection can be optimized together with heterogeneous but subtly related task, so that the detection robustness can be improved through multi-task learning.

Description

technical field [0001] The present application relates to face alignment, in particular, to methods and systems for face landmark detection. Background technique [0002] Facial keypoint detection is fundamental to many face analysis tasks, such as face attribute inference, face verification, and face recognition, but has been hampered by the problems of light occlusion and pose variation. [0003] Accurate facial keypoint detection can be performed using a cascaded CNN (Convolutional Neural Network), where the face is pre-partitioned into different parts, and each part is processed by a separate deep CNN. The resulting outputs are then averaged and passed to separate cascaded layers to process each face keypoint separately. [0004] Furthermore, facial keypoint detection is not an independent problem, and its estimation can be affected by many heterogeneous but subtly correlated factors. For example, when a child is smiling, his or her mouth is opened wide. Effectively d...

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Application Information

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IPC IPC(8): G06K9/78G06V10/764
CPCG06V40/165G06V10/82G06V10/764
Inventor 汤晓鸥张展鹏罗平吕健勤
Owner BEIJING SENSETIME TECH DEV CO LTD
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