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Double-layer-cascade-based facial feature detection method

A technology of facial features and detection methods, applied in the field of face detection, can solve the problem that the detection speed of the face detector is not ideal, and achieve the effect of reducing the false detection rate and improving the detection speed.

Active Publication Date: 2017-02-15
NANJING UNIV OF SCI & TECH
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Problems solved by technology

In general, the detection speed of the face detector obtained by SVM training is not ideal, and it is necessary to establish multiple models to improve the detection accuracy, and the combination of Boosting and DPM requires sufficient face samples with feature points

Method used

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  • Double-layer-cascade-based facial feature detection method
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Embodiment 1

[0067] combine figure 1 , the present invention is based on the facial feature detection method of double-layer cascade, and the steps are as follows:

[0068] The first level, for the input image, extract its sparse features, and quickly obtain the face candidate area:

[0069] Assume that the gradient of a pixel in the normalized image X is I in the x and y direction x , I y . The calculation formula of the gradient amplitude, gradient angle and angle channel position of the pixel is:

[0070]

[0071] θ = arctanI x / I y ∈[0,180)

[0072] bin≈θ / 20

[0073] Among them, it represents the M gradient amplitude; θ represents the gradient angle, and the value range is [0,180); bin is the angle channel position. The feature calculation steps are as follows:

[0074] (1) Read in the image, combine figure 2 (a) The normalized image size is 16×16;

[0075] (2) Calculate the I of each pixel of the image x , I y , calculate the gradient magnitude and angle of the pixel ...

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Abstract

The invention discloses a double-layer-cascade-based facial feature detection method. According to the method, a sparse feature is designed for an image including a face at a first level and a target candidate frame is obtained by using an SVM learning feature; localization of a local feature point is carried out by using a face alignment method at a second level; replacement is carried out by using a face feature point directly according to a feature extraction method using an SIFT; and then a false detection window is rejected by using a linear SVM learning feature to realize facial feature detection, wherein a result of each time is used as a sample fed back to the SVM for learning. According to the invention, because the first-layer candidate window is determined and the result of each time is used as a sample fed back to the SVM for learning, the detection speed increases; with the face alignment method, corresponding model establishment for various attitudes of the face is not required; and with combination of the high-precision SIFT feature detection method, the false detection rate is reduced effectively.

Description

technical field [0001] The invention relates to the technical field of face detection, in particular to a double-layer cascade-based facial feature detection method. Background technique [0002] Facial features refer to the key points of the face located in face detection, which is the premise and key of face image analysis. Although there are many human automatic facial analysis technologies (such as face recognition and verification, face tracking, facial expression analysis, face reconstruction and face retrieval, etc.), due to factors such as multi-pose, illumination, and occlusion of the face, Fast and accurate natural-state facial feature detection remains a major challenge. [0003] The current facial feature detection methods are mainly divided into three categories: methods based on boosting; methods based on deep convolutional neural networks; methods based on deformable models (DPM). DPM is a high-precision method that combines overall and local features and li...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V40/161G06V40/171G06V10/40G06V10/513G06V10/462G06F18/2148G06F18/2411
Inventor 吴丹丹李千目戚湧王印海
Owner NANJING UNIV OF SCI & TECH
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