PATENT CLAIM ANALYSIS

Application Number: 16337931
Application Type: Utility
Filing Date: 2019-03
Publication Date: 2019-07
Patent Classification: ["382", "118000"]

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.

Claim (Index 10):
The AU feature recognition method of  claim 9 , wherein the facial mean shape is obtained by training a facial feature recognition model, and the facial feature recognition model is an ensemble of regression trees (ERT) algorithm and is represented by a formula as follows:\n \u015c t1+1 =\u015c t1 +\u03c4 t1 ( I,\u015c t1 ), where t 1  represents a concatenation sequence number,   represents a regressor of a present stage and \u015c t1  is a shape estimate of a present model; each regressor   predicts an increment \u03c4 t1 ( I,\u015c t1 ) according to an input present image I and \u015c t1  and adds the increment to the present shape estimate to improve the present model; and in a model training process, part of feature points of all sample pictures are extracted to train a first regression tree, a second tree is trained by use of residuals between predicted values of the first regression tree and true values of the part of feature points, and so on, when residuals of predicted values of an Nth trained tree and the true values of the part of feature points are close to zero, all regression trees of the ERT algorithm are obtained, and the facial mean shape is obtained according to these regression trees.

Metadata:
- Claim Count in Document: 1.0
- Percentile: 99.0
- Lexical Diversity: 2.26471
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['16338456', '16338457', '16337934', '11062424', '14019748']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.395136848072687
- 35 USC 102 Novelty (BERT): 0.5276773896719866
- Combined Prediction Score: 0.408390902232617
- Mean Citation Score: 265.465326
- Max Citation Score: 352.45914
- Similarity Product: 274.38356542030687

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 0

Dataset: test