CN108830262A - Multi-angle human face expression recognition method under natural conditions - Google Patents

Multi-angle human face expression recognition method under natural conditions Download PDF

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CN108830262A
CN108830262A CN201810830347.7A CN201810830347A CN108830262A CN 108830262 A CN108830262 A CN 108830262A CN 201810830347 A CN201810830347 A CN 201810830347A CN 108830262 A CN108830262 A CN 108830262A
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image
angle
human face
expression recognition
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邵洁
钱勇生
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Shanghai University of Electric Power
University of Shanghai for Science and Technology
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Shanghai University of Electric Power
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/2431—Multiple classes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/174—Facial expression recognition
    • G06V40/175—Static expression

Abstract

The present invention relates to a kind of Multi-angle human face expression recognition methods under natural conditions, input the Facial Expression Image data of multi-angle, pre-process to its image data；MVFE-LightNet network structure is established, after pretreatment after image data input input layer, using two two-dimensional convolution layers, extracts the rudimentary edge feature of image；Again successively after the separable convolutional layer of 6 residual error depth, 1 two-dimensional convolution layer and 1 GlobalAveragePooling2D layers of progress image further feature extract and process；The image further feature finally extracted is sent into Softmax classifier and is trained and identifies, final classification output.Method is suitable for the facial expression recognition under multi-angle；This method arithmetic speed is fast, can be realized real-time operation；This method all has higher discrimination in different angle facial expression recognition；This method efficiently solves the network number of plies and increases and bring overfitting problem.

Description

Multi-angle human face expression recognition method under natural conditions
Technical field
The present invention relates to a kind of face recognition technology, in particular to a kind of natural shape based on MVFE-LightNet network Multi-angle human face expression recognition method under state.
Background technique
Facial expression recognition is one of the research hotspot of computer vision and area of pattern recognition, is human-computer interaction and emotion The development trend of computing technique research obtains widely in intelligent security guard, robot building, medical treatment, communication and automotive field etc. Using.The emotion of people is largely presented in human face expression, and the underlying thought of people can be judged by the variation of expression. With the development of artificial intelligence, demand of the mankind to the life of intelligent and comfortableization increasingly increases, and obtains people by human face expression The analysis of class emotion information not only has scientific research value, is also of great significance to human psychology state and affective comprehension.
In the past few decades, the research achievement of facial expression recognition is mainly for front or nearly front face image. And multi-orientation Face expression under natural conditions obviously has wider application field and higher application value.With positive dough figurine For face expression compared to identification, non-frontal face needs to handle human face posture variation bring expression information missing, multi-pose feature With the problems such as, substantially increase face acquisition, detection and identification difficulty.
Conventional face's Expression Recognition algorithm is generally divided into two steps：Feature extraction and classifier differentiate.Wherein, feature mentions Method is taken to have special based on appearance or geometrical characteristic, histograms of oriented gradients (HOG), discrete cosine transform (DCT) and Scale invariant (main includes spy for sign transformation (SIFT) etc. and their different variants, Feature Dimension Reduction (PCA, LDA and SVD) and Fusion Features Levy grade fusion and Decision-level fusion).For classifier, the classifier of most of prevalences, such as support vector machines (SVM) and shellfish This classifier of leaf, together with some unsupervised learning arts.But this kind of algorithm needs handmarking's characteristic point, it cannot be according to class With Image Adjusting feature extraction, lack robustness and practicability.If selected extraction characterization method, which lacks, distinguishes classification institute Need characterization ability, then the accuracy of disaggregated model will receive very big influence, to a certain extent with used classification policy Type it is unrelated.
Summary of the invention
The present invention be directed to the problems of non-frontal human face detection and recognition hardly possible, propose a kind of Multi-angle human under natural conditions Face expression recognition method, it can be achieved that human face posture variation greatly, the serious facial expression recognition of facial information missing.
The technical scheme is that：A kind of Multi-angle human face expression recognition method under natural conditions, specifically includes as follows Step：
1) the Facial Expression Image data for inputting multi-angle, pre-process its image data, include the following steps：
1.1) coarse localization of detection and key point is carried out to face using multitask concatenated convolutional neural network method, Human face region is cut again；
1.2) global contrast normalizes：To have already passed through Face datection and cut after original image carry out gray processing and Normalized controls the scale of each feature in corresponding range；
1.3) human face expression data expand：" enhancing " is carried out to image using stochastic transformation to handle, and picture is carried out automatically Amplification data；
2) MVFE-LightNet network frame is established：MVFE-LightNet network structure successively includes input layer, two Two-dimensional convolution layer, the separable convolutional layer of 6 residual error depth, 1 two-dimensional convolution layer, 1 GlobalAveragePooling2D Layer and Softmax classifier output layer；After pretreatment after image data input input layer, using two two-dimensional convolution layers, mention Take the rudimentary edge feature of image；Image successively is realized by the separable convolutional layer of 6 residual error depth, 1 two-dimensional convolution layer again Further feature is extracted, and image further feature passes through 1 GlobalAveragePooling2D layers again and applies the overall situation to airspace signal Average value pond after the replacement connected entirely, is sent into Softmax classifier and is trained and identifies, final classification output.
One Batch Normalization function of each convolution heel and a ReLU activate letter in the step 2) Number, wherein along with a MaxPooling2D calculating after the separable convolutional layer of residual error depth.Beneficial effects of the present invention exist In：Present invention Multi-angle human face expression recognition method under natural conditions, suitable for the facial expression recognition under multi-angle；This method Arithmetic speed is fast, can be realized real-time operation；This method all has higher discrimination in different angle facial expression recognition； This method efficiently solves the network number of plies and increases and bring overfitting problem.
Detailed description of the invention
Fig. 1 is multi-view face detection of the present invention and cutting schematic diagram；
Fig. 2 is multi-angle of view human face expression gray processing of the present invention and normalization schematic diagram；
Fig. 3 is that face expression data of the present invention expands schematic diagram；
Fig. 4 is that the present invention is based on MVFE-LightNet network model schematic diagrames；
Fig. 5 is that Conv2D convolution sample of the present invention exports schematic diagram；
Fig. 6 is that image deep layer convolution of the present invention extracts image further feature schematic diagram；
Fig. 7 is the experimental result picture of the multi-angle of view facial expression recognition confusion matrix in BU-3DFE database of the present invention.
Specific embodiment
1, human face expression data prediction
The Facial Expression Image data for inputting multi-angle, pre-process its image data, if Fig. 1,2,3 are establishing The schematic diagram of human face expression data prediction.
(1) Face datection and cutting：Using multitask concatenated convolutional neural network (Multi-Task Convolutional Neural Net-work, MTCNN) method carries out the coarse localization of detection and key point to face, then human face region is cut, Multi-view face detection as shown in Figure 1 and cutting schematic diagram.MTCNN is the nerve net of a deep layer grade (24 layers) multitask frame Network, firstly, adjustment image size constructs image pyramid, and the input as three-stage cascade frame to different scales, three The cascade structure of the deep convolutional network of grade is as follows：
P-Net(Proposal Network)：Candidate face frame and its bounding box regression vector are generated, the side is then used Boundary's frame regression vector calibrates candidate frame, and merges the candidate frame of high superposed using non-maxima suppression (NMS) method.
R-Net(Refine Network)：Candidate frame is further screened, is corrected using bounding box recurrence, and use NMS Merge candidate frame.
O-Net(Output Network)：It is similar with R-Net, best candidate frame is selected, and export the position of five characteristic points It sets.
(2) global contrast normalizes：Normalization is that the original image after having already passed through Face datection and cutting is each The scale of feature is controlled in corresponding range.Facial image carries out gray processing and normalized, multi-angle of view face as shown in Figure 2 Expression gray processing and normalization schematic diagram, with frontal one be 90 degree indicates, other angles expression 0 °, 45 °, 90 °, 135 ° with 180°.In addition to neutrality, including anger, detest, frightened, glad, sad and surprised seven kinds of basic facial expressions.
(3) data enhance
It is performed better than when deep learning model treatment large data collection.Image " increase using a series of stochastic transformations It handles by force ", so that model not can be appreciated that identical image twice, effectively improves picture utilization rate.For example, rotate, overturn, The transformation such as zooming and panning.Present invention uses width and height displacement, image overall width or height are 0.2, Random-Rotation model Enclosing is 0-20 °, and shearing range is 0.1, and zooming range is [0-0.1].Also image level is overturn, and applies " fill pattern " plan Newly created pixel is slightly filled, using the picture pretreating tool of deep learning frame keras automatically to the amplification data of picture, Human face expression data as shown in Figure 3 expand schematic diagram.
2, MVFE-LightNet network frame
The basic structure of the MVFE-LightNet network model that the present invention designs as shown in figure 4, with Xception and Based on Resnet framework, this separable convolution sum residual error network module of architecture combined depth, network parameter is reduced same When, network performance is not lost, is trained using Adam optimizer.
MVFE-LightNet network structure is a full convolutional neural networks, and MVFE-LightNet network structure is successively wrapped Include input layer, two two-dimensional convolution layers, the separable convolutional layer of 6 residual error depth, 1 two-dimensional convolution layer, 1 GlobalAveragePooling2D layers and 1 Softmax output layer.In addition to GlobalAveragePooling2D layers last, One Batch Normalization function of each convolutional layer heel and a ReLU activation primitive, wherein each residual error depth A MaxPooling2D operation is added after separable convolutional layer.Input layer is that input size is 64 × 64 × 1 pixel Multi-angle facial expression image, two two-dimensional convolution layers (Conv2D) use size to carry out sliding window volume for 3 × 38 convolution kernels Product, this layer extract the rudimentary edge feature of image, remain the details of image, Conv2D convolution sample output signal as shown in Figure 5 Figure.The image further feature of extraction image deep layer convolution as shown in Figure 6 extracts image further feature schematic diagram, a-f difference in Fig. 6 The output that the 1st to the 6th residual error depth separates convolutional layer, it can be found that more toward deep layer, expressed by information it is more abstract multiple It is miscellaneous.The last layer is that airspace signal applies global mean value pond with GlobalAveragePooling2D layers, is to be connected entirely The replacement connect reduces the quantity of parameter, and doing regularization in structure to whole network prevents over-fitting.Output layer is Softmax Classifier predicts that GlobalAveragePooling2D layers of output are sent into Softmax classifiers and are trained and identify to generate, It is finally reached classification purpose.This is popularization of the Logic Regression Models in more classification problems, and in more classification problems, k can be predicted Kind may (species number of the k for sample label, k=6 herein, angle be divided into 5 angles, are the faces for being directed to 5 angles respectively What expression was trained and identified, that is, classifier do not distinguish which angle is current expression be.In other words, we are preparatory Known facial angle is then input in the classifier of some angle and carries out Expression Recognition), it is assumed that classifier input feature vector is(Refer to n+1 dimension space.N+1 is an assumption value, that is, passes through 6 layers of separable convolution of residual error depth Output feature vector hypothesis after layer is n+1 dimension, then inputs to softmax and classifies), sample label y(i), that is, constitute Classification layer supervised learning training set S={ (x(1), y(1)), (x(2), y(2)) ..., (x(m), y(m)), it is assumed that function hθ(x) and cost function J (θ) form difference is as follows：
Wherein P is probability, conditional probability；θ is classifier parameters, has k, each n+1 dimension；It is Model parameter,For item is normalized to probability distribution, so that the sum of all probability are 1.
Wherein, 1 { }=1 is an indicative function, and value rule is：When expression formula is true in braces, the function Result be just 1, otherwise its result is just 0.
Softmax regression model is popularization of the logistic regression model in more classification problems, when number of classifying is 2 Time can degenerate for Logistic classification.In more classification problems, class label y can take more than two values.
The hypothesis function of logistic regression model is as follows：
Training pattern parameter θ can minimize cost function J (θ)：
X is inputted for given test, with assume function for each classification y=j estimate probability value p (y=j | X), that is, estimate the probability that each classification results of x occur.Assuming that function will export the vector of k dimension to indicate this k The probability value of a estimation.Assuming that function hθ(x) form is as follows：
WhereinIt is model parameter,For item is normalized to probability distribution, make Obtaining the sum of all probability is 1.
For convenience's sake, we equally indicate whole model parameters using symbol theta.Realizing Softmax recurrence When, θ is indicated with a k × (n+1) matrix can easily, which is by θ1, θ2..., θkIt is enumerated by row It arrives, as follows：
T is transposition.
In softmax regression algorithm, returning cost function is：
Wherein, 1 { }=1 is an indicative function, and value rule is：When expression formula is true in braces, the function Result be just 1, otherwise its result is just 0.
3. experimental result and analysis
The discrimination of every kind of expression in BU-3DFE database under different angle is as shown in table 1, between this 6 kinds of expressions Confusion matrix it is as shown in Figure 7.From table 1 it follows that positive, discrimination 0.837 higher than the discrimination of other angles, this Outside, whole discrimination is up to 0.887；It can be seen that in six kinds of expressions from these confusion matrixs of Fig. 7, it is surprised and glad Expression than detest and it is frightened be easier identified, be most likely to be the muscle deformation of both expressions relatively than other expressions Greatly.
Table 1
Network parameter size as shown in table 2 and training speed compare, and compare the size of several models, discrimination and speed Degree, it can be seen that the discrimination of MVFE-LightNet network model is apparently higher than LightNet and Mnist-cnn network model, And there is overfitting problem in Xcepton model.The discrimination of this paper network model is slightly below Resent-18 network model, still Runing time is about its half, and experimental period is greatly saved.
Table 2

Claims (2)

1. a kind of Multi-angle human face expression recognition method under natural conditions, which is characterized in that specifically comprise the following steps：
1) the Facial Expression Image data for inputting multi-angle, pre-process its image data, include the following steps：
1.1) coarse localization of detection and key point is carried out to face using multitask concatenated convolutional neural network method, then is cut out Cut human face region；
1.2) global contrast normalizes：Gray processing and normalizing are carried out to the original image after having already passed through Face datection and cutting Change processing controls the scale of each feature in corresponding range；
1.3) human face expression data expand：" enhancing " is carried out to image using stochastic transformation to handle, and picture is expanded automatically Data；
2) MVFE-LightNet network frame is established：MVFE-LightNet network structure successively includes input layer, two two dimensions Convolutional layer, the separable convolutional layer of 6 residual error depth, 1 two-dimensional convolution layer, 1 GlobalAveragePooling2D layers and Softmax classifier output layer；After pretreatment after image data input input layer, using two two-dimensional convolution layers, figure is extracted As rudimentary edge feature；Image deep layer successively is realized by the separable convolutional layer of 6 residual error depth, 1 two-dimensional convolution layer again Feature extraction, image further feature pass through 1 GlobalAveragePooling2D layers again and apply the overall situation averagely to airspace signal It is worth pond, after the replacement connected entirely, is sent into Softmax classifier and is trained and identifies, final classification output.
2. Multi-angle human face expression recognition method under natural conditions according to claim 1, which is characterized in that the step 2) In one Batch Normalization function of each convolution heel and a ReLU activation primitive, wherein residual error depth can divide From convolutional layer after calculate along with MaxPooling2D.
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