Face Detection Performance Research Articles

CCTV Based Observation for Face Masked Detector

The utilization of CCTV observation is presently required withinside the general public and individual areas to ensure assurance against psychological oppression and burglary. Normal articulations are utilized to demonstrate a major arrangement of development credits caught in a video. Video surveillance is a famous machine wherein vital scenes aren't captured with the aid of using human intervention. The essential intention is to mechanically discover masked human beings in much less time. In this paper, the device includes 4 distinct steps: calculating the gap variety of someone from the camera, detecting eyes or eyes, and detecting components of the face consisting of the mouth and face. The overall performance of the Viola-Jones set of rules runs on loads of real-time inputs. Experimental critiques display that the Viola-Jones set of rules is advanced in phrases of time consumption. Viola-Jones algorithm is a unique approach to this problem, it creates a transparent, complex and simple way to make real-time implementations advantageous and practical. Analysis of Viola-Jones set of rules compliance at the take a look at video music offers an affordable evaluation for extra upgrades in masked face detection performance.

Present-Ma’am: An Attendance Management System

Abstract: Attendance management is critical for every institution; it can determine whether or not an institution, such as educational institutions, public or commercial sectors, will be successful in the future. To maximise their performance, institutions will need to keep track of people within the organisation, such as employees and pupils. Real-Time Face Recognition is a useful way for dealing with the daily attendance of a vast number of pupils. To increase face detection and recognition performance, several algorithms and strategies have been developed, however, our suggested model uses the LBPH (Local binary pattern histogram) methodology, which is implemented in Python using the OpenCV organization and employs the tinker GUI interface for user interface needs.

Intelligent lecture recording system based on coordination of face-detection and pedestrian dead reckoning.

Automatic lecture recording is an appealing alternative approach to manually recording lectures in the process of online course making as it can to a large extent save labor cost. The key of the automatic recording system is lecturer tracking, and the existing automatic tracking methods tend to lose the target in the case of lecturer’s rapid movement. This article proposes a lecturer tracking system based on MobileNet-SSD face detection and Pedestrian Dead Reckoning (PDR) technology to solve this problem. First, the particle filter algorithm is used to fuse the PDR information with the rotation angle information of the Pan-Tilt camera, which can improve the accuracy of detection under the tracking process. In addition, to improve face detection performance on the edge side, we utilize the OpenVINO toolkit to optimize the inference speed of the Convolutional Neural Networks (CNNs) before deploying the model. Further, when the lecturer is beyond the camera’s field of view, the PDR auxiliary module is enabled to capture the object automatically. We built the entire lecture recording system from scratch and performed the experiments in the real lectures. The experimental results show that our system outperforms the systems without a PDR module in terms of the accuracy and robustness.

FAFD: Fast and Accurate Face Detector

Deep Neural Networks (DNN) have contributed a significant performance improvement in face detection. However, since most models focus only on the improvement of detection accuracy with computationally expensive structures, it is still far from real-time applications with a fast face detector. The goal of this paper is to improve face detection performance from the speed-focusing point of view. To this end, we propose a novel Fast and Accurate Face Detector (FAFD) to achieve high performance on both speed and accuracy performance. Specifically, based on the YOLOv5 model, we add one prediction head to increase the detection performance, especially for small faces. In addition, to increase the detection performance of multi-scale faces, we propose to add a novel Multi-Scale Image Fusion (MSIF) layer to the backbone network. We also propose an improved Copy-Paste to augment the training images with face objects in various scales. Experimental results on the WiderFace dataset show that the proposed FAFD achieves the best performance among the existing methods in a Speed-Focusing group. On three sub-datasets of WiderFace (i.e., Easy, Medium, and Hard sub-datasets), our FAFD yields average precisions (AP) of 95.0%, 93.5%, and 87.0%, respectively. Also, the speed performance of the FAFD is fast enough to be included in the group of speed-focusing methods.

A Fusion Approach Based on HOG and Adaboost Algorithm for Face Detection under Low- Resolution Images

Detecting human faces in low-resolution images is more difficult than high quality images because people appear smaller and facial features are not as clear as high resolution face images. Furthermore, the regions of interest are often impoverished or blurred due to the large distance between the camera and the objects which can decrease detection rate and increase false alarms. As a result, the performance of face detection (detection rate and the number of false positives) in low-resolution images can affect directly subsequent applications such as face recognition or face tracking. In this paper, a novel method, based on cascade Adaboost and Histogram of Oriented Gradients (HOG), is proposed to improve face detection performance in low resolution images, while most of researches have been done and tested on high quality images. The focus of this work is to improve the performance of face detection by increasing the detection rate and at the same time decreasing the number of false alarms. The concept behind the proposed combination is based on the a-priori rejection of false positives for a more accurate detection. In other words in order to increase human face detection performance, the first stage (cascade Adaboost) removes the majority of the false alarms while keeping the detection rate high, however many false alarms still exist in the final output. To remove existing false alarms, a stage (HOG+SVM) is added to the first stage to act as a verification module for more accurate detection. The method has been extensively tested on the Carnegie Melon University (CMU) database and the low-resolution images database. The results show better performance compared with existing techniques.

Human Stress Recognition from Facial Thermal-Based Signature: A Literature Survey

Stress is a normal reaction of the human organism which triggered in situations that require a certain level of activation. This reaction has both positive and negative effects on everyone’s life. Therefore, stress management is of vital importance in maintaining the psychological balance of a person. Thermal-based imaging technique is becoming popular among researchers due to its non-contact conductive nature. Moreover, thermal-based imaging has shown promising results in detecting stress in a non-contact and non-invasive manner. Compared to other non-contact stress detection methods such as pupil dilation, keystroke behavior, social media interaction and voice modulation, thermal-based imaging provides better features with clear boundaries and requires no heavy methodology. This paper presented a brief review of previous work on thermal imaging related stress detection in humans. This paper also presented the stages of stress detection based on thermal face signatures such as dataset type, thermal image face detection, feature descriptors and classification performance comparisons are presented. This paper can help future researchers to understand stress detection based on thermal imaging by presenting the popular methods previous researchers use for stress detection based on thermal images.

A Comparative Study on the Privacy Risks of Face Recognition Libraries

The rapid development of machine learning and the decreasing costs of computational resources has led to a widespread usage of face recognition. While this technology offers numerous benefits, it also poses new risks. We consider risks related to the processing of face embeddings, which are floating point vectors representing the human face in an identifying way. Previously, we showed that even simple machine learning models are capable of inferring demographic attributes from embeddings, leading to the possibility of re-identification attacks. This paper examines three popular Python libraries for face recognition, comparing their face detection performance and inspecting how much risk each library's embeddings pose regarding the aforementioned data leakage. Our experiments were conducted on a balanced face image dataset of different sexes and races, allowing us to discover biases in our results.

HDR4CV: High Dynamic Range Dataset with Adversarial Illumination for Testing Computer Vision Methods

Benchmark datasets used for testing computer vision (CV) methods often contain little variation in illumination. The methods that perform well on these datasets have been observed to fail under challenging illumination conditions encountered in the real world, in particular, when the dynamic range of a scene is high. The authors present a new dataset for evaluating CV methods in challenging illumination conditions such as low light, high dynamic range, and glare. The main feature of the dataset is that each scene has been captured in all the adversarial illuminations. Moreover, each scene includes an additional reference condition with uniform illumination, which can be used to automatically generate labels for the tested CV methods. We demonstrate the usefulness of the dataset in a preliminary study by evaluating the performance of popular face detection, optical flow, and object detection methods under adversarial illumination conditions. We further assess whether the performance of these applications can be improved if a different transfer function is used.

Research on Face Recognition Technology Based on Improved YOLO Deep Convolution Neural Network

With the development of information technology, deep learning has become a research hots pot in the field of computer vision, among which face recognition technology, as one of its applications, has received extensive attention in recent years. A face recognition method based on improved YOLOv3 deep convolution neural network is proposed. The feature pyramid network is used to obtain the four scale features of the target to fuse the shallow features and the deep features, and the influence weight of the loss function is adjusted according to the size of the detected target, so as to enhance the detection effect of small targets and mutually occluded objects, and the Softmax function is used for classification and recognition. Experimental results on mixed data sets show that this method can improve the real-time performance of face detection.

FIN-GAN: Face illumination normalization via retinex-based self-supervised learning and conditional generative adversarial network

Illumination normalization is one of the most challenging issues for facial analysis. To be specific, the variation of environmental illumination influences the visual quality of an image and drastically degrades the performance of face recognition, detection, landmark and other related applications. Retinex theory provides an important concept for processing illumination variation, which supposes that a face image can be decomposed into [an invariant reflectance component and a variant illumination component. Enlighten by this theory, in this paper, we put forward a novel deep learning approach which combines self-supervised learning and adversarial training for face illumination normalization (FIN-GAN). The proposed FIN-GAN framework can be implemented by two steps. Firstly, self-supervised learning is employed to decompose the original face image into the illumination component and the illumination-invariant component with Retinex constraint. Then, we employ the conditional generative adversarial network for face image reconstruction. For network optimization, we design the combined loss to ensure visual quality and preserve identity information. Experiments are performed on Extended-YaleB, CAS-PEAL, CMU-PIE and Multi-PIE datasets. Through multiple quantitative criteria, we demonstrate that the proposed FIN-GAN obtains promising performance in face illumination normalization.

Domain General Face Forgery Detection by Learning to Weight

In this paper, we propose a domain-general model, termed learning-to-weight (LTW), that guarantees face detection performance across multiple domains, particularly the target domains that are never seen before. However, various face forgery methods cause complex and biased data distributions, making it challenging to detect fake faces in unseen domains. We argue that different faces contribute differently to a detection model trained on multiple domains, making the model likely to fit domain-specific biases. As such, we propose the LTW approach based on the meta-weight learning algorithm, which configures different weights for face images from different domains. The LTW network can balance the model's generalizability across multiple domains. Then, the meta-optimization calibrates the source domain's gradient enabling more discriminative features to be learned. The detection ability of the network is further improved by introducing an intra-class compact loss. Extensive experiments on several commonly used deepfake datasets to demonstrate the effectiveness of our method in detecting synthetic faces. Code and supplemental material are available at https://github.com/skJack/LTW.

MTCNN and FACENET Based Access Control System for Face Detection and Recognition

Face detection and recognition is one of the research hotspots in the field of computer vision, which is widely used in video surveillance and identity matching. The traditional algorithms of face detection include AdaBoost, Haar-like, DPM, etc. These algorithms use face geometric features and template matching for face detection, which is difficult to consider both detection speed and accuracy. In order to solve this problem, this paper proposes an improved FaceNet network. The multi-task cascaded convolutional neural networks (MTCNN) is used to achieve rapid face detection and face alignment, and then the FaceNet with improved loss function is used to realize face verification and recognition with high accuracy. We compare the face detection and recognition performance of our network framework with the traditional algorithms and some deep learning algorithms. The experimental results verify that the improved FaceNet can meet the requirements of real-time recognition, and the recognition accuracy reaches 99.85%, which meets the actual demand. It can be effectively applied to face detection and recognition in the Access Control System.

Application of Face Recognition Method Under Deep Learning Algorithm in Embedded Systems

The Convolutional Neural Network (CNN) based on deep learning is introduced to propose two deep face detection algorithms and design an embedded face recognition system, in an effort to apply the deep learning algorithm to face detection and explore the embedded face recognition system. First, an optimized Multi-task Cascaded Convolutional Network (MTCNN) algorithm is proposed for the simulation transformation and crop preprocessing of the face image, denoted as OMTCNN. Second, a lightweight face recognition algorithm based on CNN is proposed to reduce the computational complexity of face recognition in the embedded system, denoted as LCNN. Finally, OMTCNN and LCNN are combined to construct the multi-core embedded face recognition system. Results demonstrate that OMTCNN shows good performance in determining face identity, and its training accuracy can reach 95.78%, significantly better than the unimproved MTCNN algorithm. On the Labeled Faces in the Wild (LFW) dataset, LCNN provides a correct rate of 98.13%, and the reduction in the complexity of the model itself increases the computational speed by as much as 6.4 times. The test results suggest that the designed face recognition system has good applicability on the embedded platform. The increase in the accuracy of the face recognition module and the introduction of the calculation acceleration module have significantly improved the face detection and recognition performance of the embedded system. The embedded face recognition system based on deep learning has practical application value.

Finding hard faces with better proposals and classifier

Recent studies witnessed that deep CNNs significantly improve the performance of face detection in the wild. However, detecting faces with small scales, large pose variations, and occlusions is still challenging. In this paper, to detect challenging faces, we present a boosted faster RCNN (F-RCN) version with an enhanced region proposal network (eRPN) module and newly introduced hard example mining strategies. The eRPN module generates better proposals than traditional RPN by integarating semantic information into the input feature maps. Two hard example mining strategies, i.e., online hard proposal mining (OHPM) and offline hard image mining (OHIM), are proposed to train better classifier. The OHPM can effectively sample quality and diversity of hard positive examples, which is important for detecting hard faces like tiny faces. The OHIM further boosts the classifier to detect hard faces via an auxiliary fine-tuning on a small proportion of training data. Experimental results on the FDDB, WIDER FACE, Pascal Faces, and AFW datasets show that our method significantly improves the faster-RCNN face detector and achieves performance superior or comparable to the state-of-the-art face detectors.

Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications.

Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require large computation power and processing time. In this work, we evaluate the speed–accuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy. We expect this to become a very useful tool for the end user in forensic laboratories in order to estimate the performance for different face detection options. Experimental results showed that the best speed–accuracy tradeoff is achieved with images resized to of the original size in GPUs and images resized to of the original size in CPUs. Moreover, performance can be estimated using multiple linear regression models with a Mean Absolute Error (MAE) of 0.113, which is very promising for the forensic field.

Automatic Identification of Individual Primates with Deep Learning Techniques.

SummaryThe difficulty of obtaining reliable individual identification of animals has limited researcher's ability to obtain quantitative data to address important ecological, behavioral, and conservation questions. Traditional marking methods placed animals at undue risk. Machine learning approaches for identifying species through analysis of animal images has been proved to be successful. But for many questions, there needs a tool to identify not only species but also individuals. Here, we introduce a system developed specifically for automated face detection and individual identification with deep learning methods using both videos and still-framed images that can be reliably used for multiple species. The system was trained and tested with a dataset containing 102,399 images of 1,040 individuals across 41 primate species whose individual identity was known and 6,562 images of 91 individuals across four carnivore species. For primates, the system correctly identified individuals 94.1% of the time and could process 31 facial images per second.

Recurrent Exposure Generation for Low-Light Face Detection

Face detection from low-light images is challenging due to limited photons and inevitable noise, which, to make the task even harder, are often spatially unevenly distributed. A natural solution is to borrow the idea from <i>multi-exposure</i>, which captures multiple shots to obtain well-exposed images under challenging conditions. High-quality implementation/approximation of multi-exposure from a single image is however nontrivial. Fortunately, as shown in this paper, neither is such high-quality necessary since our task is <i>face detection</i> rather than <i>image enhancement</i>. Specifically, we propose a novel <i>Recurrent Exposure Generation (REG)</i> module and couple it seamlessly with a <i>Multi-Exposure Detection (MED)</i> module, and thus significantly improve face detection performance by effectively inhibiting non-uniform illumination and noise issues. REG produces progressively and efficiently intermediate images corresponding to various exposure settings, and such pseudo-exposures are then fused by MED to detect faces across different lighting conditions. The proposed method, named <i>REGDet</i>, is the first &#x2018;detection-with-enhancement&#x2019; framework for low-light face detection. It not only encourages rich interaction and feature fusion across different illumination levels, but also enables effective end-to-end learning of the REG component to be better tailored for face detection. Moreover, as clearly shown in our experiments, REG can be flexibly coupled with different face detectors without extra low/normal-light image pairs for training. We tested REGDet on the DARK FACE low-light face benchmark with thorough ablation study, where REGDet outperforms previous state-of-the-arts by a significant margin, with only negligible extra parameters.

Prominent facial feature and hybrid learning method-based advanced face detector robust to head-up, head-down, and arbitrary rotation cases

Face detection is important for computer vision. Until now, the Viola and Jones algorithm is still the most popular face detector embedded in digital cameras. Although it has good performance in frontal face detection, it may not perform well in detecting rotated, head-up, and head-down faces. In this paper, we propose a more robust face detector based on prominent feature extraction. We find that, in addition to using eyes and mouth, which have been adopted by other algorithms, noses and ears are also important clues for face detection. Particularly, for a profile face, only a single eye can be detected. For head-up or head-down faces, eyes and mouth may not be well detected. However, in these cases, noses and ears can still be detected well and we can apply these features to improve the accuracy of rotated, head-up, and head-down face detection. To well detect these prominent features, in addition to the Viola–Jones detector, we also apply edge and color information and use the refocus algorithm based on the convolutional neural network. Simulations conducted on several popular multi-view face databases show that the proposed face detector can attain higher detection rates and is robust to rotated, head-up, and head-down cases.

Multi-target video-based face recognition and gesture recognition based on enhanced detection and multi-trajectory incremental learning.

BACKGROUND: Video-based face recognition (VFR) is one of the frontier topics in the domain of computer vision, which aims to automatically track and recognize facial regions of interests (ROIs) in video sequences.OBJECTIVE: In videos with multiple faces, the trajectories of individuals are incredibly complex. This is less studied than videos with a single face per frame.METHODS: In this paper, we present a multi-trajectory incremental learning (MTIL) algorithm, which categorizes trajectories using a Euclidean distance-based greedy algorithm and estimates the most likely labels for each trajectory by incremental learning to correct their classification and improve the accuracy of recognition. Furthermore, this study proposes an enhanced detection method that combines face detection with a robust tracking-learning-detection (TLD) algorithm to improve the performance of face detection in video. The method can also be extended for medical video recognition applications such as gesture recognition control based medical system.RESULTS: Experiments on Honda/UCSD and BMP (seq_mb) database demonstrate that our method can improve the face detection and face recognition (single or multiple) performance. The method also performs well on the gesture recognition system.CONCLUSION: The proposed MTIL algorithm can significantly improve the performance of the VFR system and the gesture recognition system.

Face Detection and Recognition Based on Visual Attention Mechanism Guidance Model in Unrestricted Posture

Performance of face detection and recognition is affected and damaged because occlusion often leads to missed detection. To reduce the recognition accuracy caused by facial occlusion and enhance the accuracy of face detection, 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. A large number of simulation experiment results show that our proposed method is superior to other comparison algorithms for the accuracy of occlusion face detection and recognition on the face database. In addition, our proposed method achieves a better balance between detection accuracy and speed, which can be used in the field of security surveillance.