PATENT CLAIM ANALYSIS

Application Number: 16509091
Application Type: Utility
Filing Date: 2019-07
Publication Date: 2019-10
Patent Classification: ["382", "118000"]

Abstract:
Face model matrix training method, apparatus, and storage medium are provided. The method includes: obtaining a face image library, the face image library including k groups of face images, and each group of face images including at least one face image of at least one person, k>2, and k being an integer; separately parsing each group of the k groups of face images, and calculating a first matrix and a second matrix according to parsing results, the first matrix being an intra-group covariance matrix of facial features of each group of face images, and the second matrix being an inter-group covariance matrix of facial features of the k groups of face images; and training face model matrices according to the first matrix and the second matrix.

Claim (Index 33):
The apparatus according to  claim 32 , wherein the processor is further configured to:\n calculate a third matrix S \u03bc  according to a first matrix S g  and a second matrix S s , wherein S u =con(u)=S g +S s ; initialize a fourth matrix S \u03b5 ; calculate F according to S \u03bc , wherein F=S \u03bc \u22121 , and calculate G according to S \u03bc  and S \u03b5 , wherein G=\u2212(mS \u03bc +S \u03b5 ) \u22121 S \u03bc S \u03b5 \u22121 , and m is a quantity of persons corresponding to the face images in the face image library; calculate a Gaussian distribution mean \u03bc i  of an i th  person in the face image library according to F and G, wherein \u03bc i =\u03a3 i=1 m S \u03bc (F+mG)x i , and calculate a joint distribution covariance matrix \u03b5 ij  of the i th  person and a j th  person according to F and G, wherein \u03b5 ij =x j +\u03a3 i=1 m S \u03b5 Gx i , x i  is the high-dimensional feature vector of the i th  person, and x j  is the high-dimensional feature vector of the j th  person; update S \u03bc  according to \u03bc i , and update S \u03b5  according to \u03b5 ij T  and \u03b5 ij , wherein S \u03bc = cov \ue8a0 ( \u03bc ) = m - 1 \ue89e \u2211 i \ue89e \u03bc i \ue89e \u03bc i T , S \u025b = cov \ue8a0 ( \u025b ) = m - 1 \ue89e \u2211 i \ue89e \u2211 j \ue89e \u025b ij \ue89e \u025b ij T , \u03bc i T  is a transposed vector of \u03bc i , and \u03b5 ij T  is a transposed vector of \u03b5 ij ;\n obtain S \u03bc  and S \u03b5 , if S \u03bc  and S \u03b5  are convergent; and \n re-calculate F according to S \u03bc  and re-calculate G according to S \u03bc  and S \u03b5 , if S \u03bc  and S \u03b5  are not convergent.

Metadata:
- Claim Count in Document: 14.0
- Percentile: 100.0
- Lexical Diversity: 2.67925
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['15703826', '13084406', '13239997', '13355335', '12402761']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.351734013483472
- 35 USC 102 Novelty (BERT): 0.5706759696386247
- Combined Prediction Score: 0.3736282090989873
- Mean Citation Score: 266.923878
- Max Citation Score: 483.66867
- Similarity Product: 359.26225433533966

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

Dataset: test