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In the natural scene, the human face testing technology challenges greatly, the US Mission AI platform visual intelligence center has improved two aspects of the underlying algorithm model and system architecture, developed high-precision facial test model
. Currently, the model has been used in the business line of the US group, which is highly satisfied with the performance needs of the business.
Face detection technology is automatically returned to the face coordinate position and size size in the picture through artificial intelligence analysis. It is the core component of face intelligent analysis applications. It has extensive academic research value and business application value, such as face recognition. , Face attribute analysis (age estimate, gender identification, color value score and expression recognition), face avatar, smart video surveillance, face image filtering, smart image cut, face AR game, etc. Due to the scene of shooting, the natural scene environment is complicated and more varied. The illuminance is not controllable, and the face itself has a large challenge to the detection task (as shown in Figure 1). Over the past 20 years, this task has always been a hot spot in the academic community and the industry.
Natural scene people also have a wide range of applications in the US group business, in order to cope with the technical challenge of the natural scene application itself, while meeting the performance requirements of the business, the US Group Vision Intelligence Center (VIC) from the underlying algorithm model I have improved two aspects of the system architecture, developed high-precision facial detection models
. Moreover, VicFace reached the mainstream level of the industry on the internationally renowned public evaluation set Wider Face.
Figure 1 Natural scene face detection sample example
Second, the status quo of technology development
Unlike deep learning, traditional methods solve natural scene facial detection will design two aspects of feature representations and classifier. The most representative work is Viola-Jones algorithm , which utilizes manually designed Haar-Like features and AdaBoost algorithms to complete model training. The traditional method is fast detection speed on the CPU, and the results can be interpretable, and better performance can be achieved in a relative controllable environment. However, when the training data scale is exponentially increasing, the performance of the traditional method is relatively limited, in some complex scenes, and even unable to meet the application needs.
With the improvement of computer power, the growth of training data has achieved breakthrough progress in face detection tasks, which has an overwhelming advantage in detection performance. Face detection algorithm based on depth learning can be roughly divided into three categories:
1) Based on cascaded face detection algorithm.
2) Two-stage face detection algorithm.
3) Single stage face detection algorithm.
Among them, the first class is based on cascading face detection method (such as Cascade CNN , MTCNNN ) runs faster, moderate detection performance, suitable for limited integration, simple background, less faceful Scenes. The second type of two-stage face detection method is generally based on the FASTER-RCNN  framework, generating a candidate area in the first phase, and then classifies and regained the candidate area in the second phase, and the detection accuracy is high, the disadvantage is detection The speed is slow, representing the method with Face R-CNN , Scaleface , FDNET . The last type of single-stage face detection method is based primarily on Anchor’s classification and regression, and it is usually optimized based on the classic frame (such as SSD , RetinaNet ), and its detection speed is faster than two stages. Detecting performance is superior to the level of association, is an algorithm for detecting performance and speed balance, and is also the mainstream direction of current face detection algorithm optimization.
Third, optimize ideas and business applications
In the natural scene application, in order to meet the accuracy requirements and achieve the practical goal, the US Group Vision Center (VIC) uses the mainstream Anchor-based single-stage face test scheme, and in the data enhancement and sampling strategy. , Model structural design and loss functions have been optimized, and high-precision facial detection models have been developed.
. The following is an introduction to the details of the relevant technique.
1. Data enhancement and sampling strategy
Single-stage general purpose target detection algorithm is more sensitive to data enhancements, such as classic SSD algorithms on VOC2007  data sets to increase 6.7 through data enhancement performance indicator MAP. The classic single-stage face detection algorithm S3FD  also designed sample enhancement strategy, using pictures random cut, picture fixed aspect ratio zoom, image color disturbance and horizontal flip, etc.
Baidu Pyramidbox  published by ECCV2018 proposed a Data-Anchor sampling method, and a randomly selected face in the image beats a smaller ANChor’s face, while the size of the training image also performs synchronous transformation . The advantage of this is to generate a smaller face in a smaller face by increasing the smaller face, and increased 0.4 on the Wider Face  data set Easy, Medium, and HARD collections. (94.3- > 94.7), 0.4 (93.3-> 93.7), 0.6 (86.1 -> 86.7). ISRN  combines SSD sample enhancement mode and Data-Anchor sampling method, and the model detection performance is further improved.
VICFACE is filtered on the Semantic blurred ultra-faced face on the basis of the ISRN sample enhancement, and Mixup  has been valid in image classification and target detection, which is now used for face detection and effectively prevents it. Model over fitting problem. Considering that there are many postures, occlusion, and blurred samples in business data, and these samples are small in training, and the detection is difficult, so the dynamics give these difficult samples to give higher weights during model training. Sample recall rate.
2. Model structure design
Face detection model structure design mainly includes four parts of the detection framework, backbone network, prediction module, Anchor settings and positive and negative samples, and is the core of single-stage face detection method optimization.
In recent years, a single-stage face detection framework has achieved important development, representative structures with SSDs used in S3FD , used in Retinanet, SRN  used in SFDET  (referred to as SRN) and Double Structure (After DSFD) used in DSFD , as shown in Figure 2 below. Among them, SRN is a single-stage two-step face detection method, using the first step detection result, the negative samples of the smaller person, improve the equalization of the positive and negative samples, people for large scale The face is positioned in a manner, improves the positioning accuracy of large-scale face, and improves the accuracy of face detection. The Evaluation SRN has achieved the best detection effect on Wider Face (measured by AP accuracy by standard protocol), as shown in Table 1.
Figure 2 four detection structures
Table 1 When BackBone is RESNET50, the results of the four detection structures on Wider Face
VicFace inherits the best SRN detection structure of current performance, and for better fusion of the upward and auto-down feature, different weights are given different weights for different characteristics, as an example of P4, which is:
Where the number of elements of WC4 vectors is equal to the channel number of CONV (C4) features, WP4 is equal to the number of channels of UpSample (P5), and WC4 and WP4 are learning, and the elements of elements are greater than 0, and WC4 corresponds to WP4. The sum is 1, the structure is shown in Figure 3.
Figure 3 Vision Intelligent Center VICFACE Network Overall Structure
The backbone of the single-stage face detection model typically uses the classic structure in the classification task (such as VGG , ResNet , etc.). Among them, the linkage of the backbone of the backbone network is based on the IMAGENET data set, the higher the face detection performance on Wider Face, as shown in Table 2. In order to ensure that the detection network is higher recall, VICFACe’s RESID network uses the RESNet152 network in ImageNet (which is 80.26 on the ImageNet on the ImageNet), and it will be 7×7 on the ImageNet to TOP1 Classification Accounting. The convolution module of Stride is adjusted to 3 3×3 convolution modules, where the first module’s stride is 2, the other is 1; the KERNEL is 1×1, the Stride 2 is replaced with Stride 2 to Stride 2 Avgpool module.
Table 2 Different backbone networks in ImageNet performance comparison and their detection accuracy under the RetinaNet framework
The detection performance of the model can be further improved by using context information. SSH  is an early program, Pyramidbox, SRN, DSFD, etc. of the single-stage face detection model, also designed different context modules. As shown in FIG. 4, the SRN context module provides a variety of rectangular perfoster wild, and a plurality of different shapes of experience in detecting extreme postures using 1xK, KX1, and has a human face that detects extreme posture; DSFD uses a plurality of convolutions with holes. Greatly enhanced the range of experience wilderness.
Figure 4 Context module in different network structures
In VicFace, the convolutional module and 1xk, KX1 convolution modules are combined as Context Module, which is also improved in the range of perception and helps to detect the human face of extreme posture, and use the maxout module to improve the recall rate, lowered Misrode. It also utilizes the face position predicted by the CN layer, calibrates the area corresponding to the PN layer characteristics, as shown in FIG. The offset of the face position relative feature position predicted by the CN layer is an offset input, and the PN layer feature acts as a variable convolved DATA input. The region corresponding to the human face area corresponds to the human face area. Ok, relatively more representative, can enhance the performance of the face detection model.
Figure 5 prediction module in self-study detection model structure
Anchor settings and positive and negative samples
In the self-developing scheme, at the C3, P3 layers, the ANChor size is 2S and 4S, the other layer Anchor size is 4s (S represents the stride of the corresponding layer), such anchor setting mode while ensuring the person’s face call return rate, reduced The number of negative samples is mitigated to a certain extent. According to the statistics of the face-like as high ratio, the aspect ratio of Anchor is set to 0.8, and the sample of the CN layer IOU is more than 0.7 is divided into a positive sample, and the division of less than 0.3 is negative sample, the PN layer IOU is greater than 0.5 sample It is divided into a positive sample, less than 0.4 is divided into negative samples.
3. Loss function
The optimization goal of face detection requires not only a distinguishing spray (whether it is a face), but also needs to position the face position and size. Distinguishing between S3FD Use intersecting loss functions, locating face position and size using Smooth L1 Loss, while using difficult negative samples excavation to solve the problem of unbalanced samples. Another way to alleviate the performance loss of positive and negative samples is a more direct way to propose Focal Loss . UnitBox  proposes that IOU LOSS can alleviate performance loss in the positioning loss of different scales. Alnnoface  uses Focal Loss and IOU LOSS to enhance the performance of face detection models. Introducing other related auxiliary tasks can also enhance the performance of the face detection algorithm, retinaface  introduce key positioning tasks, enhance the positioning accuracy of the face detection algorithm; DFS  introduces face segmentation tasks, enhances the characteristics of the characteristics .
The advantages of the aforementioned method, VicFace take advantage of the complementary information of face detection and related tasks, and use multi-tasking methods to train face detection models. Use Focal LOSS in face classification to alleviate sample imbalances, while using face-critical positioning and face segmentation to assist in classification targets, thereby improving overall classification accuracy. Using Complete IOU LOSS  in face positioning, the difference between the target and the prediction box is the loss function, alleviate the difference between different scale face losses, while taking care of the center point distance of the target and prediction box and The difference in aspect ratio can achieve better overall detection performance.
4. Optimize results and business applications
Under the support of the cluster platform, the natural scene of the US Visual Intelligence Center is compared with the existing mainstream program, and the three verification sets of the International Open Face Detection Evaluation Collection Wider Face Easy, Medium, The lead is reached in Hard (AP is average accuracy, the higher the value, the better), as shown in Figure 6 and Table 3.
Figure 6 Evaluation results of VicFace and current mainstream face detection method on Wider Face
Table 3 VicFace and current mainstream face detection method in Wider Face
Note: SRN is a new method proposed by the Chinese Academy in Aaai2019. DSFD is a new method proposed in CVPR2019. PyramidBox ++ is a new method proposed in 2019. Ainnoface is a new method proposed in 2019. Retinaface is ICCV2019 Wider challenge runner.
In business applications, the natural scene face detection service has been connected to multiple business lines in the US group, which meets the performance requirements of the business in UGC image intelligence filtering and advertising POI image display, the former protects user privacy, preventing user portraits The right, the latter can effectively prevent the phenomenon of local croping in the image, thereby improving the user experience. In addition, VicFace also provides a core basic model for other face intelligent analysis applications, such as automatic detection of kitchen workers’ fitness (wearing hats and masks), adding a guarantee for food safety.
In future work, in order to give users a better experience, it will meet further exploration and optimization in terms of high-concurrent demand. In addition, in an algorithm design, the single-stage target detection method based on Anchor-free has exhibited high potential in the field of general purpose target detection in recent years, and is also an important direction in the future of visual intelligence.
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About the Author
Zhenhua, Huanhuan, Xiaolin,
All are the visual intelligent center engineers.
The main responsibility of the US Mission Visual Intelligent Center Basic Visual Group is to consolidate the core basic technology of visual intelligence underground, providing a platform-level visual solution for the group business. There are basic model optimizations, large-scale distributed training, SERVER efficiency optimization, mobile adaptation optimization and innovative product incubation.
Welcome to the computer visual related field small partners to join us, resume can be sent to Tech@meituan.com (the message title indicates: US Group Vision Intelligent Center Basic Group).