Face Detection Algorithm Research Articles

Recognition and Prediction of Badminton Attitude Based on Video Image Analysis

Among many sports, badminton is one of the most popular events and it is deeply loved by people. However, there is relatively little research on pose recognition and prediction of badminton, so this paper uses video image analysis to perform badminton action recognition prediction and action classification. In order to better realize the lower limb movement pattern recognition and motion posture prediction of badminton players, this paper chooses BP neural network algorithm to establish a badminton motion posture recognition and prediction model based on video image analysis. The simulation results of the models are compared, and it is found that the recognition of the motion pose and the prediction model established by the neural network are accurate, and they almost coincide with the actual motion trend. In this paper, a face detection and recognition framework is established. The algorithm is used to filter the pictures of the input recognition network. Through calculation, the average accuracy rate of the face detection algorithm realized in this paper can reach 92.6%. This shows that the face detection algorithm implemented in this paper basically meets the standards. In this paper, a single inertial sensor is used to classify and recognize badminton movements using sensors located on the right wrist, left wrist, waist, and right ankle. This shows that the right wrist is the best position and achieves different strokes.

Multi-feature based automatic facial expression recognition using deep convolutional neural network

<p><span>Deep multi-task learning is one of the most challenging research topics widely explored in the field of recognition of facial expression. Most deep learning models rely on the class labels details by eliminating the local information of the sample data which deteriorates the performance of the recognition system. This paper proposes multi-feature-based deep convolutional neural networks (D-CNN) that identify the facial expression of the human face. To enhance the accuracy of recognition systems, the multi-feature learning model is employed in this study. The input images are preprocessed and enhanced via three filtering methods i.e., Gaussian, Wiener, and adaptive mean filtering. The preprocessed image is then segmented using a face detection algorithm. The detected face is further applied with local binary pattern (LBP) that extracts the facial points of each facial expression. These are then fed into the D-CNN that effectively recognizes the facial expression using the features of facial points. The proposed D-CNN is implemented, and the results are compared to the existing support vector machine (SVM). The analysis of deep features helps to extract the local information from the data without incurring a higher computational effort.</span></p>

Online Classroom Attendance Marking System Using Face Recognition, Python, Computer Vision, and Digital Image Processing

Abstract: The face is the identity of someone. The tactic to appear out this physical feature has seen an exquisite change since the advent of the image processing method. The attendance is taken in every school, college and library. The regular method for attendance is teachers calling student name & marking attendance. Nowadays, AI has been highly explored for computer vision applications[1]. So, we use the concept of neural network in Face – recognition for automatic attendance marking systems. In this project, we perform the face recognition and face detection algorithms, to produce the computer systems the ability to find and recognize human faces fast and precisely in live videos so that the systems can be used in marking attendance. Keywords: Face detection, Python, Computer Vision, Attendance marking system, Image processing

Facial Emotion Recognition as Spatial Image using Gabor Filter

The state of mind of a person can be easily understood from the human face. This paper proposes a methodology to recognize facial expression using Gabor filters, ResNet and two other custom models. The image is taken as the input data from the camera. This input can be used to extract information to infer a person's mood. First, we develop an algorithm for detecting image of an individual from entire set of images using Haar Cascade face detection algorithm. Then, we apply Gabor filter for extracting facial features in the spatial domain. Using the Gabor filter can effectively reduce computation and size, and in some situations even improve recognition. Gabor filters are used to capture the entire frequency spectrum in all directions. Finally, facial expressions are successfully classified by proposed Convolutional Neural Network model using extracted important facial features from the facial image after applying Gabor filter as input. The results of testing images from the CK+ dataset show the reliability and the best recognition rate of the proposed method.

Methods and algorithms for protecting information in optical text recognition systems

The subject of the study. A concept of OCR systems performance improvement is proposed, which is achieved through the integrated use of special algorithms for preliminary processing of documents picture, an extended set of service functions, and advanced techniques for information protection. Study objectives: development of algorithms that compensate for the influence of the unfavorable points like imperfect lighting conditions overshooting, images geometric deformation, noises, etc., which corrupt the pictured text, on the efficiency of that text recognition. It is needed to provide for a series of service procedures that would ensure adequate data handling while viewing, converting, and storing in standard formats the results, and ensuring the possibility to exchange data in open communication networks. Additionally, it is necessary to ensure the information protection against unauthorized use at the stage of data processing and provide secretiveness of their transmission through the communication channels. Investigation methods and research results: developed and tested algorithms for preliminary picture data processing, namely, for the captured image geometry transformation, picture noise correction with different filters, image binarization when using the adaptive thresholds reduced the negative influence of irregular image portions illumination; in the software, the special services ensure the data processing ease and information protection are affected. In particular, the interactive procedure for text segmentation is proposed, which implies the possibility of anonymizing its fragments and contributes to collecting confidentiality for documents treated. The package for processing document shots contains the face detection algorithm bringing the identification of such information features; it can be used further in the task of face recognition. After the textual doc is recognized, the received data encryption is provided by generating a QR-code and the steganography methods can deliver the privacy of this information transmission. The algorithms' structures are described in detail and the stability of their work under various conditions is investigated. Focused on the case study, docs' text recognition software was developed with the usage of Tesseract version 4.0 optical character recognition program. The program named "HQ Scanner" is written in Python using the present resources of the OpenCV library. An original technique for evaluating the efficiency of algorithms using the criterion of the maximum probability of correct text recognition is implemented in the software. Conclusions. The study results can constitute the basis for developing advanced specialized software for easy-to-use commercial OCR systems.

Video Face Detection Technology and Its Application in Health Information Management System

Computer face detection, as an early step and prerequisite for applications such as face recognition and face analysis, has attracted people's attention for a long time. With the popularization of computer applications, the improvement of performance, and the gradual maturity of research in the field of image processing and pattern recognition, face-related applications have become more and more a reality, so the research on face detection and positioning is also receiving more and more attention. Face detection and positioning are an important part of face analysis technology. Its goal is to search for the location of facial features (such as eyes, nose, mouth, and ears) in images or image sequences. It can be widely used in the fields of face tracking, face recognition, gesture recognition, facial expression recognition, head image compression and reconstruction, facial animation, etc. Based on the health information management system, this study mainly discusses the application of face recognition technology in video systems. Compared with other biological characteristics, such as fingerprints and eye masks, human faces are easier to obtain. In research and exploration, stable and effective face detection and face recognition algorithms have been proposed, which can achieve good recognition results even in real-time video surveillance. Aiming at the automatic face recognition technology in video surveillance, this study introduces in detail the video face detection technology in the health information management system of video image collection, image preprocessing, face detection, and face recognition. The prototype system of hygiene management is recognized.

Face Detection Algorithm Based on Double-Channel CNN with Occlusion Perceptron.

Aiming at the problem of low accuracy of face detection under complex occlusion conditions, a double-channel occlusion perceptron neural network model was proposed. The area occlusion judgment unit is designed and integrated into the VGG16 network to form an occlusion perceptron neural network. Thereupon, the features of unoccluded regions and less occluded regions in facial images are extracted by the perceptual neural network. Transfer learning algorithm is utilized to pretrain parameters of the convolution layer to reduce the overfitting problem caused by insufficient training data samples. Face features of the whole face were extracted by optimizing the residual network, and then the face features of the occluding perceptron neural network and the residual network were weighted and fused. Experiments were carried out on two open data sets, AR and MAFA. The results demonstrate that the detection accuracy of this method is higher than that of other methods, and the detection speed is faster.

Fast and Robust People Detection in RGB Images

People detection in images has many uses today, ranging from face detection algorithms used by social networks to help the users tag other people, to surveillance systems that can create a statistic of the population density in an area, or identify a suspect, or even in the automotive industry as part of the Pedestrian Crash Avoidance Mitigation (PCAM) system. This work focuses on creating a fast and reliable object detection algorithm that will be trained on scenes that depict people in an indoor environment, starting from an existing state-of-the-art approach. The proposed method improves upon the You Only Look Once version 4 (YOLOv4) network by adding a region of interest classification and regression branch such as Faster R-CNN’s head. The candidate bounding boxes proposed by YOLOv4 are ranked based on their confidence score, the best candidates being kept and sent as input to the Faster Region-Based Convolutional Neural Network (R-CNN) head. To keep only the best detections, non-maximum suppression is applied to all proposals. This decreases the number of false-positive candidate bounding boxes, the low-confidence detections of the regression and classification branch being eliminated by the detections of YOLOv4 and vice versa in the non-maximum suppression step. This method can be used as the object detection algorithm in an image-based people tracking system, namely Tracktor, having a higher inference speed than Faster R-CNN. Our proposed method manages to achieve an overall accuracy of 95% and an inference time of 22 ms.

Classroom face detection algorithm based on improved MTCNN

Classroom face detection algorithm based on improved MTCNN

Video Face Detection Based on Improved SSD Model and Target Tracking Algorithm

Video face detection technology has a wide range of applications, such as video surveillance, image retrieval, and human-computer interaction. However, face detection always has some uncontrollable interference factors in the video sequence, such as changes in lighting, complex backgrounds, and face changes in scale and occlusion conditions, etc. Therefore, this paper introduces deep learning theory and combines the continuity characteristics of video sequences to make related research on video face detection algorithms based on deep learning. First, this algorithm uses the residual network as the basic network of the Single Shot MultiBox Detector (SSD) target detection network model and trains a Rest-SSD face detection model to detect faces. Experimental results show that the method can achieve real-time detection and improve the accuracy of video face detection, which is required for face detection in a video. Then we based on the continuity characteristics of video sequences. This paper proposes a video face detection method based on the training of the Rest-SSD face detection model. The method first uses kernel correlation filtering to track consecutive n frames according to the detection results, sets weights on the confidence of the n frames of tracking results, uses the weighted average method to calculate the best tracking result, and then sets the best tracking result confidence and the current frame sets the appropriate weights for the confidence of the detection result for fusion, thereby improving the video face detection accuracy.

Research on Face Detection Algorithm Based on Improved YOLOv5

Research on Face Detection Algorithm Based on Improved YOLOv5

Image Processing for Improvement of Facial Keypoints Detector

This paper discusses the improvement of facial detection algorithms using the DCT algorithm and image processing. Face key point is very needed in the face recognition system. Some important factors that have effects to detect its result are noise and illuminations. These two factors can be overcome by eliminating some DCT coefficients, both high and low. However, after handling the problem, most likely the image quality will become decrease, which will adversely influence the performance of the feature detector algorithm. Therefore, it is very important to test the performance of the feature detector algorithm on images that are implemented noise and illumination handling and how to improve the quality again. This research implemented Discrete Cosine Transform (DCT), by eliminating the high and low coefficient because there is noise and illumination. However, it is not known at what coefficient level is the most effective, so testing in this study was carried out. Four deblurring algorithms are tested in this research, Blind Deconvolution, Wiener Filter Deconvolution, Lucy-Richardson Deconvolution, and Regularization Deconvolution. And tested the CLAHE algorithm to overcome the effect of removing low coefficient DCT. The best coefficient value to be removed at the DCT frequency is 0.75 with the best SURF algorithm, without the use of other algorithms. Also, the highest F-score is produced by the SURF detector at removing DCT low frequency in combination with the CLAHE algorithm. With the most ideal coefficient of 0.25.

ACWFace: efficient and lightweight face detector based on RetinaFace

Lightweight face detection algorithms that typically utilize convolutional neural network to find out all faces from the entire vision range. However, compared with more accurate and heavy algorithms, the performance of existing lightweight networks is still left far behind. Toward this end, we propose a lightweight and efficient single-stage face detector, named ACWFace, which explores the effects of attention, context module, and weighted feature fusion based on RetinaFace. First, efficient dual attention module is designed to further explore the potential of channel attention and spatial attention by introducing adaptive convolution kernel. Second, extended context module and shuffled context module are proposed to enlarge the receptive field and increase the information intersection between branches. Finally, weighted-fusion feature pyramid network is utilized to solve the features fusion of different scales equally by introducing the feature fusion module. Experiments on the easy, medium, and hard datasets of WIDER FACE validation partition show that our ACWFace outperforms RetinaFace average precision by 1.0%, 1.1%, and 1.8% while it achieves a great growth of 0.6%, 6.5%, and 3.0% on annotated faces in the wild, PASCAL face, and face detection data set and benchmark datasets, respectively.

Face detection and recognition in organic video: A comparative study for sport celebrities database

The present paper reports on an experimental study carried out under the applicative field of organic video processing, and relates to the possibility of identifying sport celebrities (soccer players) in video content. In contrast to common state-of-the-art studies, a special attention is paid on the cases in which the face is not completely included in the frame (lateral views, partial occlusions, etc.) and/or in which arbitrarily lighting conditions occur. To this aim, we consider two conventional types of face detection algorithms (Haar Cascade Classifier, and MMOD - Max-Margin object detection) coupled to two conventional face recognition models (LSBH - Local binary pattern histogram, and CNN-based Pruned ResNet). The experimental work consists of evaluating the end-to-end performances of the four possible combinations among the above-mentioned two face detection and two face recognition methods. A database of 20 video sequences of about 3 minutes each is organized. As an overall conclusion, we brought to light that the MMOD coupled to a Pruned ResNet model seems to better suit the organic video processing use-case constraints, being able to reach a recognition rate of 98%.

An Efficient Multi-Scale Anchor Box Approach to Detect Partial Faces from a Video Sequence

In recent years, face detection has achieved considerable attention in the field of computer vision using traditional machine learning techniques and deep learning techniques. Deep learning is used to build the most recent and powerful face detection algorithms. However, partial face detection still remains to achieve remarkable performance. Partial faces are occluded due to hair, hat, glasses, hands, mobile phones, and side-angle-captured images. Fewer facial features can be identified from such images. In this paper, we present a deep convolutional neural network face detection method using the anchor boxes section strategy. We limited the number of anchor boxes and scales and chose only relevant to the face shape. The proposed model was trained and tested on a popular and challenging face detection benchmark dataset, i.e., Face Detection Dataset and Benchmark (FDDB), and can also detect partially covered faces with better accuracy and precision. Extensive experiments were performed, with evaluation metrics including accuracy, precision, recall, F1 score, inference time, and FPS. The results show that the proposed model is able to detect the face in the image, including occluded features, more precisely than other state-of-the-art approaches, achieving 94.8% accuracy and 98.7% precision on the FDDB dataset at 21 frames per second (FPS).

Soft Transducer for Patient's Vitals Telemonitoring with Deep Learning-Based Personalized Anomaly Detection.

This work addresses the design, development and implementation of a 4.0-based wearable soft transducer for patient-centered vitals telemonitoring. In particular, first, the soft transducer measures hypertension-related vitals (heart rate, oxygen saturation and systolic/diastolic pressure) and sends the data to a remote database (which can be easily consulted both by the patient and the physician). In addition to this, a dedicated deep learning algorithm, based on a Long-Short-Term-Memory Autoencoder, was designed, implemented and tested for providing an alert when the patient’s vitals exceed certain thresholds, which are automatically personalized for the specific patient. Furthermore, a mobile application (EcO2u) was developed to manage the entire data flow and facilitate the data fruition; this application also implements an innovative face-detection algorithm that ensures the identity of the patient. The robustness of the proposed soft transducer was validated experimentally on five individuals, who used the system for 30 days. The experimental results demonstrated an accuracy in anomaly detection greater than 93%, with a true positive rate of more than 94%.

A web-based application for face detection in real-time images and videos

Face detection is widely used in the consumer industry such as advertising, user interfaces, video streaming apps and in many security applications. Every application has its own demands and constraints, and hence cannot be fulfilled by a single face detection algorithm. In this work, we developed an interactive web-based application for face detection in real-time images and videos. Pretrained face detection algorithms, namely Haar cascade classifier, HOG-based frontal face detector, Multi-task Cascaded Convolutional Neural Network (MTCNN) and Deep Neural Network (DNN), were used in the web-based application. A performance analysis of these face detection algorithms is done for various parameters such as different lighting conditions, face occlusion and frame rate. The web app interface can be used for an easy comparison of different face detection algorithms. This will help the user to decide on the algorithm that suits their purpose and requirements for various applications.

Secure Rotation Invariant Face Detection System for Authentication

Biometric applications widely use the face as a component for recognition and automatic detection. Face rotation is a variable component and makes face detection a complex and challenging task with varied angles and rotation. This problem has been investigated, and a novice algorithm, namely RIFDS (Rotation Invariant Face Detection System), has been devised. The objective of the paper is to implement a robust method for face detection taken at various angle. Further to achieve better results than known algorithms for face detection. In RIFDS Polar Harmonic Transforms (PHT) technique is combined with Multi-Block Local Binary Pattern (MBLBP) in a hybrid manner. The MBLBP is used to extract texture patterns from the digital image, and the PHT is used to manage invariant rotation characteristics. In this manner, RIFDS can detect human faces at different rotations and with different facial expressions. The RIFDS performance is validated on different face databases like LFW, ORL, CMU, MIT-CBCL, JAFFF Face Databases, and Lena images. The results show that the RIFDS algorithm can detect faces at varying angles and at different image resolutions and with an accuracy of 99.9%. The RIFDS algorithm outperforms previous methods like Viola-Jones, Multi-block Local Binary Pattern (MBLBP), and Polar Harmonic Transforms (PHTs). The RIFDS approach has a further scope with a genetic algorithm to detect faces (approximation) even from shadows.

Novel face detection algorithm with a mask on neural network training

Discovering objects and knowing their number has been discussed in many works. Face detection technology is important for the visual scene, Deep learning theory using computer technology to discover the face, which is a wide field in marketing, traffic and security system control systems, in addition to photography. Facial recognition algorithms or face detection Include steps for the facial image to extract features to match them with a database. The face has a biometric feature. The facial feature consists of prominent and easily identifiable information that is responsible for distinguishing the objects that distinguish the face, the distance between the eyes, the shape of the nose, and The mouth for the device to perform a training group and record the data. Matlab program helps to dispense with training because MATLAB provides the instruction (CascadeObjectDetector) for facial recognition and the Viola-Jones algorithm with the result of separating the detection results in the form of subsets. In this work, a new algorithm is created for a group of elements in the unit picture And run a face detection code to highlight the background to store the information of each image in a specified folder and the face detection techniques by proposing a new algorithm to detect a face from among a group of faces, distinguish it and make it a file of its own, all of that using the Matlab program to train the neural network for face recognition.

Face detection algorithm under low-light based on feature recovery

Face detection algorithm under low-light based on feature recovery