Face Detection Algorithm Research Articles

Performance Tuning Techniques for Face Detection Algorithms on GPGPU

Face detection algorithms varies in speed and performance on GPUs. Different algorithms can report different speeds on different GPUs that are not governed by linear or near-linear approximations. This is due to many factors such as register file size, occupancy rate of the GPU, speed of the memory, and speed of double precision processors. This paper studies the most common face detection algorithms LBP and Haar-like and study the bottlenecks associated with deploying both algorithms on different GPU architectures. The study focuses on the bottlenecks and the associated techniques to resolve them based on the different GPUs specifications.

Face Expression Recognition using Convolution Neural Network (CNN) Models

This paper proposes the design of a Facial Expression Recognition (FER) system based on deep convolutional neural network by using three model. In this work, a simple solution for facial expression recognition that uses a combination of algorithms for face detection, feature extraction and classification is discussed. The proposed method uses CNN models with SVM classifier and evaluates them, these models are Alex-net model, VGG-16 model and Res-Net model. Experiments are carried out on the Extended Cohn-Kanada (CK+) datasets to determine the recognition accuracy for the proposed FER system. In this study the accuracy of AlexNet model compared with Vgg16 model and ResNet model. The result show that AlexNet model achieved the best accuracy (88.2%) compared to other models.

Semiautomated Class Attendance Monitoring Using Smartphone Technology

Class attendance is important. Class attendance recording is often done using ‘roll-call’ or signing attendance registers. These are time consuming, easy to cheat and it is difficult to draw any information from them. There are other, expensive alternatives to automate attendance recording with varying accuracy. This study experimented with a smart phone camera and different combinations of face detection and recognition algorithms to determine if it can be used to record attendance successfully, while keeping the solution cost-effective. The effect of different class sizes was also investigated. The research was done within a pragmatism philosophy, using a prototype in a field experiment. The algorithms that were used, are: Viola-Jones (HAAR features), Deep Neural Network (DNN) and Histogram of Oriented Gradients (HOG) for detection and Eigenfaces, Fisherfaces and Local Binary Pattern Histogram (LBPH) for recognition. The best combination was Viola-Jones combined with Fisherfaces, with a mean accuracy of 54% for a class of 10 students and 34.5% for a class of 22 students. The best all over performance on a single class photo was 70% (class size 10). As is, this prototype is not accurate enough to use, but with a few adjustments, it may become a cheap, easy-to-implement solution to the attendance recording problem.

Raspberry Pi based Intelligent Classroom Information and Management System using LBP Method

Educational organizations are gradually using multi prevention models to deal with tutorial requirements of pupils. The underpinning of those methods is that the execution of widespread practices premeditated to backing the tutorial requirements of the overwhelming widely held scholars. Investigators and experts are trying to valid measures to support teachers for effective classroom practices, which are sensitive to vary over time. Knowing the information about the classwork, students’ strength in a particular class, faculty details, passing the vital information to the students, alerting faculty with their classwork information, and automatic attendance system plays a key role in the smooth running of educational institutes or organizations. Particularly, in women colleges, passing the information about the ward to the parents and video surveillance is crucial. This paper aims at providing solutions for manual work to a great extent. The proposed system works using RaspberryPi 3 board with the display, face detection algorithm, face recognition using Local Binary Pattern method (LBP), GSM900, 4x4 Keyboard, mobile, and serial communication protocols. The new entrant to the campus will easily know about the college information and punctual working of the stakeholders in the campus. The unauthorized entry is detected using a webcam, and information is passed to the central unit for further action.

A smart login system using face detection and recognition by ORB algorithm

<p>We can identify human faces using a web Camera which is known as Face Detection. This is a very effective technique in computer technology. There are used different types of attendance systems such as log in with the password, punch card, fingerprint, etc. In this research, we have introduced a facial recognition type of biometric system that can identify a specific face by analyzing and comparing patterns of a digital image. This system is the latest login system based on face detection. Primarily, the device captures the face images and stores the captured images into the specific path of the computer relating the information into a database. When any body tries to enter into any room or premises through this login system, the system captures the image of that particular person and matches the image with the stored image. If this image matches with the stored image then the system allows the person to enter the room or premises, otherwise the system denies entry. This face recognition login system is very effective, reliable and secured. This research has used the Viola and Jones algorithm for face detection and ORB for image matching in face recognition and Java, MySql, OpenCV, and iReport are used for implementation.</p>

Multi-View Face Detection in Open Environments using Gabor Features and Neural Networks

Multi-view face detection in open environments is a challenging task, due to the wide variations in illumination, face appearances and occlusion. In this paper, a robust method for multi-view face detection in open environments, using a combination of Gabor features and neural networks, is presented. Firstly, the effect of changing the Gabor filter parameters (orientation, frequency, standard deviation, aspect ratio and phase offset) for an image is analysed, secondly, the range of Gabor filter parameter values is determined and finally, the best values for these parameters are specified. A multilayer feedforward neural network with a back-propagation algorithm is used as a classifier. The input vector is obtained by convolving the input image and a Gabor filter, with both the angle and frequency values equal to π/2. The proposed algorithm is tested on 1,484 image samples with simple and complex backgrounds. The experimental results show that the proposed detector achieves great detection accuracy, by comparing it with several popular face-detection algorithms, such as OpenCV’s Viola-Jones detector.

Research of Face Detection Based on Skin Color Feature

Face image contains many pattern features and this paper studies a face detection algorithm based on skin color features. Through the analysis of the face detection method based on skin color features, the calculation process and principle are understood, and the algorithm and detection techniques such as color space, preprocessing face image detection technology. Building a corresponding skin color model based on face characteristics are analyzed in depth. Because the skin color only occupies a specific range in the primary color of color space, the probability likelihood graph of skin color is obtained by calculating skin color similarity. Then the binary image of the possible face region is obtained by smooth filtering, threshold segmentation, and other operations. Then the area center and height ratio are used to calculate the center of mass and deflection angle, and the correlation coefficient is matched with the face module. Finally, the face area is detected and marked. Test results show that the algorithm can achieve face detection well.

Component-based face recognition using statistical pattern matching analysis.

The aim of this research is to develop a fusion concept to component-based face recognition algorithms for features analysis of binary facial components (BFCs), which are invariant to illumination, expression, pose variations and partial occlusion. To analyze the features, using statistical pattern matching concepts, which are the combination of Chi-square (CSQ), Hu moment invariants (HuMIs), absolute difference probability of white pixels (AbsDifPWPs) and geometric distance values (GDVs) have been proposed for face recognition. The individual grayscale face image is cropped by applying the Viola–Jones face detection algorithm from a face database having variations in illumination, appearance, pose and partial occlusion with complex backgrounds. Doing illumination correction through histogram linearization technique, the grayscale face components such as eyes, nose and mouth regions are extracted using the 2D geometric positions. The binary face image is created by applying cumulative probability distribution function with Otsu adaptive thresholding method and then extracted BFCs such as eyes, nose and mouth regions. Five statistical pattern matching tools such as the standard deviation of CSQ values with probability of white pixels (PWPs), standard deviation of HuMIs with Hu’s seven moment invariants, AbsDifPWPs and GDVs are developed for recognition purpose. GDVs are determined between two similar facial corner points (FCPs) and nine FCPs are extracted from binary whole face and BFCs. Pixel Intensity Values (PIVs) which are determined using L2 norms from grayscale values of the whole face and grayscale values of the face components. Experiment is performed using BioID Face Database on the basis of these pattern matching tools and appropriate threshold values with logical and conditional operators and gives the best expected results from true positive rate perspective.

Universal super-resolution for face and non-face regions via a facial feature network

This study proposes a fusion method for universal super-resolution (SR) that produces good results for both face and non-face regions. We observed that most general-purpose SR networks fail to sufficiently reconstruct facial features. In addition, face-specific networks degrade the performance on non-face regions. To reconstruct face regions well, face-specific SR networks are trained by employing a facial feature network. Then, to preserve the performance on the non-face regions, a region-adaptive fusion that uses both face-specific and general-purpose networks is proposed. In the fusion stage, a face detection algorithm is included, and detected masks are smoothed to avoid boundary artefacts. The results indicate that the proposed method significantly improves the performance on the face region and delivers a similar performance on the non-face region. In addition, the experimental results indicate that additional computations can be considerably reduced by sharing the front layers of the networks.

Efficient Half Face Detection System Based on Linear Binary Pattern

In the field of computer vision, face detection algorithms achieved accuracy to a great extent, but for the real time applications it remains a challenge to maintain the balance between the accuracy and efficiency i.e., to gain accuracy computational cost also increases to deal with the large data sets. This paper, propose half face detection algorithm to address the efficiency of the face detection algorithm. The full face detection algorithm consider complete face data set for training which incur more computation cost. To reduce the computation cost, proposed model captures the features of the half of the face by assuming that the human face is symmetric about the vertical axis passing through the nose and train the system using reduced half face features. The proposed algorithm extracts Linear Binary Pattern (LBP) features and train model using adaboost classifier. Algorithm performance is presented in terms of the accuracy i.e., True Positive Rate (TPR), False Positive Rate (FTR) and face recognition time complexity.

Illumination-robust face recognition based on deep convolutional neural networks architectures

<p><span>In the last decade, facial recognition techniques are considered the most important fields of research in biometric technology. In this research paper, we present a Face Recognition (FR) system divided into three steps: The Viola-Jones face detection algorithm, facial image enhancement using Modified Contrast Limited Adaptive Histogram Equalization algorithm (M-CLAHE), and feature learning for classification. For learning the features followed by classification we used VGG16, ResNet50 and Inception-v3 Convolutional Neural Networks (CNN) architectures for the proposed system. Our experimental work was performed on the Extended Yale B database and CMU PIE face database. Finally, the comparison with the other methods on both databases shows the robustness and effectiveness of the proposed approach. Where the Inception-v3 architecture has achieved a rate of 99, 44% and 99, 89% respectively.</span></p>

Research on Multi-feature Adaptive Fusion Face Tracking Algorithm

Under the premise of fixed computer performance, it is necessary to take into account the accuracy and real-time of face tracking. For this detection requirement, the image processing method of skin color segmentation is incorporated into AdaBoost’s face detection algorithm to accurately and quickly locate the face position. However, in the previous stage of face detection, there is a case where the real-time detection does not respond fast enough and fails the detection. In this case, a particle filter tracking algorithm based on multi-feature adaptive fusion is proposed, which uses the face area detected in the first frame as the tracking target, and achieves face detection by self-adjusting CS-LBP and the weight of how the skin color influences tracking effect, in which way the computational efficiency of face detection between frames is improved when the detection accuracy is maintained. And it is also robust to various complex external factors. It has been proved by experiments.

Real Time Automatic Attendance System for Face Recognition Using Face API and OpenCV

Traditionally, the attendance of students has been a major concern for the colleges and the faculty has to spend a lot of time and is a tedious job to mark attendance manually. Current biometric attendance system is not automatic that’s why wastes a lot of time, difficult to maintain and requires a queue for scanning fingerprints to mark their attendance. In Modern era everyone has Smartphone and connected via internet every time. In this paper attendance monitoring will be done through smart phone available with almost all faculty members. Some of popular object detection algorithms are back propagation neural network, region based convolution network (RCNN), faster RCNN, single shot detector. Our unified structure is based on YOLO V3 (You only look once) algorithm for face detection and Microsoft Azure using face API for face recognition (face database). The unique part is camera installed in classroom will take picture twice one at the start and one at the end to ensure students has attended complete class. YOLO V3 will first count the students in an image followed by identifying faces as known and unknown generating spreadsheets separately and an email is send at the end of month to students, parents and faculty. The designed system performs efficient in real time implementation for counting and detection. Our entire system has proven to gather high accuracy in face detection and performance.

Pixel encoding for unconstrained face detection

In an uncontrolled environment, many of the face detection algorithms lack robustness due to their design. The present research on unconstrained face detection is focused on handcrafted and visual features in isolation. We propose a novel approach to use handcrafted as well as visual features together for improvement in face detection to achieve robustness. The algorithm uses a side-view face detector, which divides the problem space into two: side view face detection and frontal face detection. For frontal faces, Discrete Wavelet Transform (DWT) followed by the encoding of Eyes like landmarks (ELL) pixels is proposed in this work. A Human trait that helps to make decisions even better when they are taken together with the help of more than one decision makers is modeled in this work. To achieve this, a combination of handcrafted and visual features is used. Further to improve classification and to provide a better decision, a faster second stage classification scheme is introduced. The result shows an improvement when handcrafted and visual features are combined instead of using them separately.

Development and Optimization of Check-in System Based on Face Recognition Technology

Face recognition technology is widely used. By comparing the speed and accuracy of Face++ and Baidu AI face recognition, the face sign-in service was collected in the mobile phone or pad APP, which realized the mobile check-in attendance that can be used for college students or training institutions. The system was characterized by accuracy, fast and anti-cheating ability. The experimental results show that Baidu AI’s face detection and recognition performance is better, its speed is more than twice faster than Face++, the recognition score is higher than Face++, and includes live detection, which can effectively prevent static picture cheats. However, Baidu AI’s face detection time (about 271.2ms) was much higher than the commercial face detection algorithm (about 25ms), so the Adaboost face detection algorithm was studied, and the Adaboost algorithm based on OpenCV was transplanted to Android. The platform enables real-time detection of faces. After the system optimization, the face detection time is about 10ms, which effectively improves the face detection speed.

Driver Distraction Behavior Detection Method Based on Deep Learning

With the rapid development of road traffic in China, driver safety accidents caused by road traffic accidents are increasing year by year. According to statistics of relevant departments, 20%-30% of traffic safety accidents are caused by distracted behaviors of drivers. For this reason, this paper proposes a driver distraction behavior detection method based on deep learning, which uses PCN and DSST algorithms for face detection, location and dynamic face tracking. Finally, YOLOV3 object detection algorithm is used to identify distracting behaviors such as smoking and making phone calls around a person’s face. The method can detect distracted behaviors in the driving process in real time and has high detection accuracy.

Rotating invariant face detection via cascaded networks and pyramidal optical flows

In the unconstrained open-space, face detection is still a challenging task due to the facial posture changes, complex background environment, and motion blur. The rotation-invariant algorithm based on cascaded network and pyramid optical flow is proposed. Firstly, the cascading progressive convolutional neural network is adopted to locate the face position and facial landmark of the previous frame in the video stream. Secondly, the independent facial landmark detection network is used to reposition the current frame, and the optical flow mapping displacement of the facial landmark between the two frames is calculated afterwards. Finally, the detected face is corrected by the mapping relationship between the optical flow displacement of the facial landmark and the bounding box, thereby completing the rotation-invariant face detection. The experiment was tested on the FDDB public datasets, which proved that the method is more accurate. Moreover, the dynamic test on the Boston head tracking database proves that the face detection algorithm can effectively solve the problem of rotation-invariant face detection. Compared with other detection algorithms, the detection speed of the proposed algorithm has a great advantage, and the window jitter problem in the video is well solved.

Automatic Caption Generation from Images Based on Facial Emotions

In the current era, the vast number of internet users accesses the social network sites like Facebook, Whatsapp, Twitter, and Instagram for uploading their images. User may upload all kind of images with different emotions. In this research article, we introduce novel facial emotion recognition technique using Convolutional Neural Network (CNN) approach and associated a caption automatically based on the identified emotion. We consider only still images for emotion classification. Using face detection algorithms faces were detected from the facial image which is followed by emotion prediction. We automated this detection process for images having frontal faces. In images having non-frontal faces, we manually plot the eye points for rotating the face in such a way that algorithm can detect faces easily. Inferences obtained as a result of experiments shows that the proposed work is capable of identifying minute differences between different emotions and predict accordingly. Based on the emotions identified corresponding captions are generated with better accuracy.

Research on Mobile Payment Recognition Technology Based on Face Detection Algorithm

Research on Mobile Payment Recognition Technology Based on Face Detection Algorithm

Energy-efficient face detection and recognition scheme for wireless visual sensor networks

Energy-efficient and robust face detection and recognition scheme can be useful for many application fields such as security and surveillance in multimedia and visual sensor network (VSN). VSN consists of wireless resources-constrained nodes that are equipped with low-energy CMOS cameras for monitoring. On the one hand, captured images are meaningful multimedia-data that impose high energy consumption to be processed and transmitted. On the other hand, visual sensor (VS) is a battery-powered node with limited life-time. This situation leads to a trade-off between detection-accuracy and power-consumption. This trade-off is considered as the most major challenge for applications using multimedia data in wireless environments such as VSN. For optimizing this trade-off, a novel face detection and recognition scheme has been proposed in this paper based on VSN. In this scheme, detection phase is performed at VS and recognition phase is accomplished at the base station (sink). The contributions of this paper are in three folds: 1. Fast and energy-aware face-detection algorithm is proposed based on omitting non-human blobs and feature-based face detection in the considered human-blobs. 2. A novel energy-aware and secure algorithm for extracting light-weight discriminative vector of detected face-sequence to be sent to sink with low transmission-cost and high security level. 3. An efficient face recognition algorithm has been performed on the received vectors at the sink. The performance of our proposed scheme has been evaluated in terms of energy-consumption, detection and recognition accuracy. Experimental results, performed on standard datasets (FERET, Yale and CDnet) and on personal datasets, demonstrate the superiority of our scheme over the recent state-of-the-art methods.