In order to evaluate the performance of the face detection and recognition algorithm based on the visual attention-guided mechanism proposed in this paper, LFW labeled faces in the wild database , CMUFD database CMU face detection database [ 23 ], and UCFI database UCD color face image [ 25 ] are used as face detection and recognition data sets.
In order to verify the generalization of our proposed method, some testing experiments are performed on the UCFI data set. Face objects of all training data have been accurately marked. Except for the object area, the rest is marked as background, which means that the labeled data set can be used for training and testing of face detection and recognition models. The network proposed in this paper selects pretrained ResNet50 as the backbone network. The Adam optimization algorithm is an extension of the stochastic gradient descent algorithm, which can iteratively update the neural network weights based on the training data; the initialization of learning rate is set to 0.
The nonmaximum suppression algorithm NMS is used to filter out the redundant face results. The threshold of Intersection over Union IoU is set to 0. Our proposed network module is based on the PyTorch deep learning framework. The experimental environment is Xeon Xeon E v2 2. At present, the video surveillance intelligent analysis system has been able to detect and recognize the face with different scales.
However, existing algorithms have a large number of false detection for face objects under partial occlusion mainly due to incomplete occlusion of face objects and the similarity of the face and background gray.
Therefore, the standard evaluation indicators are selected as performance evaluation, which is the false positive per image FPPI of each image, focusing on the frequency of occurrence of false positive, as shown in Table 1. In the detection stage, the evaluation criteria are the detection rate DR and the false detection rate false positive per image, FPPI : where TP represents the number of positive samples detected correctly, represents the number of positive samples included the image, and represents the number of false positive samples.
In addition, we also use the log-average miss rate MR to characterize the performance of the detector for the face. This paper mainly focuses on the occlusion situation for face detection and recognition. Therefore, we define the visible range to characterize the occlusion situation of the face. Given that the proportion of the object visible area to the total area is , if , it means that the object is in a normal state and is denoted as N ; if , it means that the object is in a serious occlusion state and is denoted as H.
For ease of analysis, we will also divide the data set into four types of subsets, which are, respectively, recorded as mixed face data set mixed , bare occlusion face data set bare , partially occlusion data set partial , and severely occlusion data set heavy [ 34 ]. Baseline adopts the feature fusion method consistent with FPN to build the model, where CA means the channel attention module and SA means the space attention module, and the performance of the detector after adding each module is compared in the experiment, and the test results are shown in Table 2.
In order to more intuitively understand the attention module on the performance of the face detector, Figure 5 gives visualized face prediction results. Through observation, it can be found that the feature response in Figure 5 d is closer to the face visible area, while there is still background interference in Figures 5 b and 5 c , but the interference of Figure 5 c is significantly less than that of Figure 5 b.
This also proves that the attention module can guide the network to highlight to the visible part of the occlusion face, while also reducing the impact of background noise on detection performance. Since the recognition algorithm based on deep learning has achieved great results in the field of natural image, some classical deep learning algorithms will be used to make a comparison with our proposed algorithm. From the experiment results in Table 3 , it can be seen that the face detection results proposed in this paper are better than the DNET mainly due to the improvement of the face detection accuracy of the attention perception fusion module and the use of the multiscale pyramid pooling layer to capture high-level semantic features.
The complementary features can effectively preserve the clear boundary of the face, while the combination of the multiple side output and pyramid pooling layer output can extract rich global context information and adapt to the two classification problems of face recognition. The heavy data set contains the most complex face image in the whole testing data. The face is seriously occluded, especially the image contrast is small and the face is fuzzy, which directly affects the detection and recognition effect of the network.
From the precision comparison results in Table 3 , it can be seen that the detection rate on the heavy data is the lowest mainly because the occlusion greatly reduces the perception ability of the deep network. However, the face detection based on the visual attention-guided mechanism proposed in this paper is also better than other deep networks.
It can be seen that our proposed algorithm achieves If the input image with a smaller resolution is detected, the detection speed of this method will be further improved. In the selected detection image, the shape and scale of the face are quite different, especially the gray level of the face and the adjacent background is similar.
However, it is proved that the face with serious occlusion such as deformation and low contrast can be accurately detected and recognized. Although it can balance the contradiction between robustness and speed, it is still easy to be disturbed by occlusion, resulting in deviation of the detection center. From the detection results, it can be seen that the ConvNet detection and recognition has deviated from the face center.
Our proposed model in this paper uses the attention-guided mechanism to highlight the visual area of occluded faces so that our proposed algorithm in this paper can better adapt to the influence of occluded interference in face detection.
In order to verify the generalization performance of the proposed method, the proposed method was trained on the LFW training set, and cross-data set experiments were performed on the CMUFD database. The heavy subset consists of face objects with a height greater than 50 pixels and a visible range of [0. As shown in Figure 6 , FPPI represents the statistical results of face detection and recognition algorithms at different detection rates. The recognition rate of the ResNet algorithm is The reason is that most deep detection methods only use the side-output feature and ignore the importance of global structural features.
Our proposed paper uses a visual attention mechanism to guide the model to highlight the occlusion object visible area and simplify the face detection and recognition problem to a high-level semantic feature detection problem through an improved analytical network and uses the activation map to predict the location and scale of the face, which can avoid additional parameter settings and further reduce the false detection rate of each image.
It can be clearly observed from Figure 6 that the performance of the proposed algorithm is obviously another algorithm. In order to improve the accuracy of face detection and recognition, a visual attention mechanism guidance model is proposed in this paper, which uses the visual attention mechanism to guide the model highlight the visible area of the occluded face.
The face detection problem is simplified into the high-level semantic feature detection problem through the improved analytical network, and the location and scale of the face are predicted by the activation map to avoid additional parameter settings. In addition, our proposed method achieves a better balance between accuracy and speed, which can be used in the field of security surveillance.
However, the performance of the proposed algorithm is sensitive to parameters, and its generalization is not high. How to improve this problem will be more conducive to the model applied to other scenarios or data. This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article of the Year Award: Outstanding research contributions of , as selected by our Chief Editors. Read the winning articles. Journal overview. Special Issues. Academic Editor: Chenxi Huang. Received 14 Apr Revised 28 Apr Accepted 06 May Published 20 May Abstract Performance of face detection and recognition is affected and damaged because occlusion often leads to missed detection. Introduction There are still some challenging problems in face detection and recognition technology mainly due to the nonrigid features and the influence of complex background [ 1 — 3 ].
Figure 1. Figure 2. Figure 3. Figure 4. Table 1. Table 2. Comparative analysis of visual attention network verification results in the face detection module. Figure 5. Performance comparison for face detection.
Table 3. Figure 6. References A. Colmenarez and T. View at: Google Scholar T. Kondo and H. Koh, S. Ranganath, and Y. View at: Google Scholar S. Chaudhry and R. Siddiqi, R. Ali, A. Khan, E. Kim, G. Kim, and S. Zhang, X. Zhu, Z. Lei, X. Wang, H. Shi, and S. Ling, J. Wu, J. Huang, J. Chen, and P. Wang, D. Zhang, and Z. Corrow, A. Albonico, and J. Abbad, O. Elharrouss, K. Abbad, and H. Madhavan and N. View at: Google Scholar Y. Su, Z. Liu, and M. Yan, S. Shan, R. Wang, Z. Cui, and X.
Zhang, K. Shang, J. Wang, N. Li, and M. Jiang, F. Chung, S. Deng, J. Wang, and P. Zhang, Y. Huang, S. Yu, and L. Liu, P. Fieguth, G. Zhao, M. View at: Google Scholar H. Shao, S. Chen, J. Zhao, W. Cui, and Y. Bobak, A. Dowsett, and S. Liu, M. Kan, W. Wu, S. Shan, and X. Rikhtegar, M. Pooyan, and M. Zhao, Y. Lv, Z. Ranked 1 on Face Identification on MegaFace. One of the main challenges in feature learning using Deep Convolutional Neural Networks DCNNs for large-scale face recognition is the design of appropriate loss functions that enhance discriminative power.
Ranked 2 on Face Verification on MegaFace. The dataset was collected with three goals in mind: i to have both a large number of identities and also a large number of images for each identity; ii to cover a large range of pose, age and ethnicity; and iii to minimize the label noise. In this paper, we design a benchmark task and provide the associated datasets for recognizing face images and link them to corresponding entity keys in a knowledge base. Face Recognition Image Captioning.
In this paper, we propose Fawkes, a system that helps individuals inoculate their images against unauthorized facial recognition models. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Read previous issues. You need to log in to edit. You can create a new account if you don't have one. Or, discuss a change on Slack.
Parent task if any : Facial Recognition and Modelling. Description with markdown optional : Facial recognition is the task of making a positive identification of a face in a photo or video image against a pre-existing database of faces. Higher is better for the metric. Uses extra training data. Data evaluated on.
This is a preview of subscription content, log in to check access. Yang and T. Moghaddam and A. Google Scholar. Soulie, E. Viennet, and B. Sung and T. Rowley, S. Baluja, and T. Burl, and P. Yow, and R. Automatic Face and Gesture Recognition, Vermont, Colmenarez and T. Chellapa, C.
Wilson, and S. Antonio J. Colmenarez 1 Thomas S. Huang 1 1. Compare to traditional AdaBoost face detection algorithm and feature subspace algorithm, the detection rate of the algorithm mentioned in this paper is not only higher nearly 10 percent, the detection velocity is also improved by more than one times. Also it is very robust to the illumination, pose etc variations and suitable for the real-time face detection system. Article :. DOI: Need Help?
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|Nursing school admission essay samples||Koh, S. Sarfraz and R. Our proposed model in this paper uses the attention-guided mechanism to highlight the visual area of occluded faces so that our proposed algorithm in this paper can better adapt to the influence of occluded interference in face detection. In this paper, the position, height, and position offset of the face are firstly predicted, and the size of the e resume builder box is obtained by simple geometric transformation, and then, the simple recognition network can get the high-precision recognition effect [ 31 ]. Aiming at the problem that occlusion affects the accuracy of face detection and recognition, this paper proposes a deep network with multilevel feature fusion. Multiple bounding boxes are predicted e resume builder each grid, and each predicted bounding box is scored to demonstrate that the bounding box completely contains the confidence of the object, which is defined as follows: where indicates the probability of the object contained in the bounding box. Colmenarez 1 Thomas S.|
|Face detection research papers||The Adam optimization algorithm is an extension of the stochastic gradient descent algorithm, which can iteratively update the neural network weights based on the training data; the initialization of learning rate is set to 0. The recognition rate of the ResNet face detection research papers is Shi, and S. Elharrouss, K. The spatial attention module obtains the spatial attention map from the spatial information of the statistical feature map, which is used to reactivate the input features, so as to guide the network to focus on the occluded face and suppress the background interference. Moghaddam e resume builder A.|
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If any person's face from public place matches, the system will display their image on the system screen and will give the message with their name that the criminal is found and present in this public place. In facial identification tasks, face alignment is the most important aspect.
Utilizing localization of various face landmarks for structural face normalization, in particular, has proven to be extremely reliable, significantly enhancing Utilizing localization of various face landmarks for structural face normalization, in particular, has proven to be extremely reliable, significantly enhancing recognition performance. Along with that, the tools are essential for building applications concerning facial recognition in real-world aspects.
The popular part of applications for face recognition such as localization of landmarks, estimating the facial position, and Multi-directional viewable face recognition are also discussed. Of all the existing repositories, AFLW is highly popular due to the existence of a high number of faces in real-world aspects. Detection and localization of face in colour images with skin tone regions involves many challenges.
This paper proposes an algorithm that uses a combination of wavelets, edge detection techniques to improve the efficiency during This paper proposes an algorithm that uses a combination of wavelets, edge detection techniques to improve the efficiency during segmentation and feature extraction stage. Experimental results using the proposed algorithm presented here show improved false acceptance and false rejection rates.
Automatic recognition of facial expressions in image sequences: A review. For human beings, facial expression is one of the most powerful and natural way to communicate their emotions and intensions. A human being can detect facial expressions without effort, but for a machine it is very difficult. Automatic facial expression recognition is an interesting and challenging problem.
Automatic facial expression recognition systems can be mainly used for human computer interaction and data driven animation. There are three sub problems while designing automatic facial expression recognition system, face detection, extraction of the facial expression information, and classification of the expression. A system that performs these operations more accurately and in real time would be crucial to achieve a human-like interaction between man and machine.
This paper reviews the past work done in solving these problems for image sequences. Grouping edge points into line segments by sequential Hough transformation. An algorithm to group edge points into digital line seg-ments with Hough transformation is described in this paper. The edge points are mapped onto the parameter domain dis-cretized at specific intervals, on which peaks appear to The edge points are mapped onto the parameter domain dis-cretized at specific intervals, on which peaks appear to rep-resent different line segments.
By modeling each An algorithm to group edge points into digital line seg- ments with Hough transformation is described in this paper. The edge points are mapped onto the parameter domain dis- cretized at specific intervals, on which peaks appear to rep The edge points are mapped onto the parameter domain dis- cretized at specific intervals, on which peaks appear to rep- resent different line segments.
By modeling each peak as a Gaussian function in the parameter domain, a region, to which the. Compact representation of PET 3D system response matrices. A comparison of face and facial feature detectors based on the Viola-Jones general object detection framework. The human face provides useful information during interaction; therefore, any system integrating Vision-Based Human Computer Interaction requires fast and reliable face and facial feature detection.
Different approaches have focused on Different approaches have focused on this ability but only open source implementations have been extensively used by researchers. A good example is the Viola—Jones object detection framework that particularly in the context of facial processing.
Face detection using improved LBP under bayesian framework. In recent years, many face detection methods have been published . Among them, appearance-based approaches show the best performance in the face detection task, such as Adaboost approach by viola , sparse network of winnows Among them, appearance-based approaches show the best performance in the face detection task, such as Adaboost approach by viola , sparse network of winnows SNoW , Neural Network [ Related Topics.
Authors Authors and affiliations Antonio J. Colmenarez Thomas S. This is a preview of subscription content, log in to check access. Yang and T. Moghaddam and A. Google Scholar. Soulie, E. Viennet, and B. Sung and T. Rowley, S.