AU FEATURE RECOGNITION METHOD AND DEVICE, AND STORAGE MEDIUM
First Claim
1. An electronic device, comprising:
- a memory, a processor and a photographic device, an action unit (AU) feature recognition program being stored in the memory and the AU feature recognition program being executed by the processor to implement the following steps of;
a real-time image capturing step;
acquiring a real-time image shot by the photographic device and extracting a real-time facial image from the real-time image by use of a face recognition algorithm;
a facial feature point recognition step;
inputting the real-time facial image into a pretrained facial mean shape and recognizing t facial feature points from the real-time facial image by use of the facial mean shape, wherein the training steps for the facial mean shape comprising;
establishing a first sample library with n facial images and marking t facial feature points at positions of eyes, eyebrows, noses, mouths and facial outer contours in each of the facial images; and
training a facial feature recognition model by use of the t facial feature points to obtain the facial mean shape, where t represents a concatenation sequence number;
a local feature extraction step;
determining feature regions matched with each AU in the real-time facial image according to positions of the t facial feature points, extracting local features from the feature regions and generating multiple feature vectors; and
an AU feature prediction step;
inputting the multiple feature vectors into pretrained AU classifiers matched with the feature regions respectively to obtain a prediction result of recognition of the corresponding AU features from the feature regions.
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Abstract
The disclosure discloses an action unit (AU) feature recognition method, which includes: acquiring a real-time image shot by a photographic device and extracting a real-time facial image from the real-time image by use of a face recognition algorithm; inputting the real-time facial image into a pretrained facial mean shape and recognizing t facial feature points from the real-time facial image by use of the facial mean shape; determining feature regions matched with each AU in the real-time facial image according to positions of the t facial feature points, extracting local features from the feature regions and generating multiple feature vectors; and inputting the multiple feature vectors into pretrained AU classifiers matched with the feature regions respectively to obtain a prediction result of recognition of the corresponding AU features from the feature regions. The disclosure also discloses an electronic device and a computer-readable storage medium.
10 Citations
24 Claims
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1. An electronic device, comprising:
- a memory, a processor and a photographic device, an action unit (AU) feature recognition program being stored in the memory and the AU feature recognition program being executed by the processor to implement the following steps of;
a real-time image capturing step;
acquiring a real-time image shot by the photographic device and extracting a real-time facial image from the real-time image by use of a face recognition algorithm;a facial feature point recognition step;
inputting the real-time facial image into a pretrained facial mean shape and recognizing t facial feature points from the real-time facial image by use of the facial mean shape, wherein the training steps for the facial mean shape comprising;establishing a first sample library with n facial images and marking t facial feature points at positions of eyes, eyebrows, noses, mouths and facial outer contours in each of the facial images; and
training a facial feature recognition model by use of the t facial feature points to obtain the facial mean shape, where t represents a concatenation sequence number;a local feature extraction step;
determining feature regions matched with each AU in the real-time facial image according to positions of the t facial feature points, extracting local features from the feature regions and generating multiple feature vectors; andan AU feature prediction step;
inputting the multiple feature vectors into pretrained AU classifiers matched with the feature regions respectively to obtain a prediction result of recognition of the corresponding AU features from the feature regions. - View Dependent Claims (3, 4, 5)
- a memory, a processor and a photographic device, an action unit (AU) feature recognition program being stored in the memory and the AU feature recognition program being executed by the processor to implement the following steps of;
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2. (canceled)
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6-8. -8. (canceled)
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9. An action unit (AU) feature recognition method, comprising:
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a real-time image capturing step;
acquiring a real-time image shot by the photographic device and extracting a real-time facial image from the real-time image by use of a face recognition algorithm;a facial feature point recognition step;
inputting the real-time facial image into a pretrained facial mean shape and recognizing t facial feature points from the real-time facial image by use of the facial mean shape, wherein the training steps for the facial mean shape comprising;establishing a first sample library with n facial images and marking t facial feature points at positions of eyes, eyebrows, noses, mouths and facial outer contours in each of the facial images; and
training a facial feature recognition model by use of the t facial feature points to obtain the facial mean shape, where t represents a concatenation sequence number;a local feature extraction step;
determining feature regions matched with each AU in the real-time facial image according to positions of the t facial feature points, extracting local features from the feature regions and generating multiple feature vectors; andan AU feature prediction step;
inputting the multiple feature vectors into pretrained AU classifiers matched with the feature regions respectively to obtain a prediction result of recognition of the corresponding AU features from the feature regions. - View Dependent Claims (10, 11, 12, 13, 16, 21, 22, 23, 24)
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14-15. -15. (canceled)
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17-20. -20. (canceled)
Specification