Robust Face Detection Research Articles

Face Detection under Hostile Circumstances using Innovative Robust Methods

Abstract: An attendance system is crucial for monitoring student presence in classes, with various methods available such as biometric, RFID card, face recognition, and traditional paper-based systems. Among these, face recognition stands out for its security and efficiency. This research focuses on enhancing the face recognition attendance system's accuracy by minimizing false positives through a confidence threshold based on the Euclidean distance metric. The Local Binary Pattern Histogram (LBPH) algorithm outperforms other distance-based methods like Eigenfaces and Fisherfaces due to its robustness against grayscale transformations. Face detection relies on Haar cascades for their robustness, while LBPH is employed for recognition. Leveraging LBPH for recognition tasks significantly improves the system’s ability to discern subtle variations in facial features, thereby enhancing overall accuracy. Moreover, the effectiveness of the system heavily relies on robust face detection techniques. Haar cascades are employed for this purpose, leveraging their proven reliability in detecting faces across diverse environmental conditions and camera angles. The system achieves a recognition rate of 77% for students and a false- positive rate of 28%. Notably, it effectively recognizes students even with variations like wearing glasses or facial hair. Face recognition for unknown individuals is approximately 60%,with false-positive rates of 14% and 30% with and without the threshold, respectively.

Robust face detection with limited visual input does not elicit saccadic response

Robust face detection with limited visual input does not elicit saccadic response

Wide aspect ratio matching for robust face detection.

Recently, anchor-based methods have achieved great progress in face detection. They adopt standard anchor matching strategy to sample positive anchors according to predefined IoU threshold. However, the max IoUs of extreme aspect ratio faces are still lower than fixed positive threshold, leading to the sampling failure from these faces. To construct a more robust detection model, more positive anchors from extreme aspect ratio faces need to be sampled and participate in the training phase. The goal of the present research is to improve the detection performance by reasonably extending sampling range of face aspect ratio. In this paper, we firstly explore the factors that affect the max IoU of each face in theory. Then, anchor matching simulation is performed to evaluate the sampling range of face aspect ratio. Finally, we propose a Wide Aspect Ratio Matching (WARM) strategy to collect more representative positive anchors from ground-truth faces across a wide range of aspect ratios. Besides, we present a novel feature enhancement module, named Receptive Field Diversity (RFD) module, to provide diverse receptive field corresponding to different aspect ratios. Extensive experiments have been conducted on popular benchmarks to show the effectiveness of our method, which can help detectors better capture extreme aspect ratio faces. Our method achieves promising APs on WIDER FACE validation dataset (easy: 0.965, medium: 0.955, hard: 0.904) and impressive generalization capability on FDDB dataset.

Comparison of Real-Time Face Detection and Recognition Algorithms

With the phenomenal growth of video and image databases, there is a tremendous need for intelligent systems to automatically understand and examine information, as doing so manually is becoming increasingly difficult. The face is important in social interactions because it conveys information. Detecting a person's identity and feelings Humans do not have a great deal of ability to identify. Machines have different faces. As a result, an automatic face detection system is essential.in face recognition, facial expression recognition, head-pose estimation, and human–computer interaction, and so on Face detection is a computer technology that determines the location and size of a person's face. It also creates a digital image of a human face. Face detection has been a standout topic in the science field This paper provides an in-depth examination of the various techniques investigated for face detection in digital images. Various face challenges and applications. This paper also discusses detection. Detection features are also provided. In addition, we hold special discussions on the practical aspects of developing a robust face detection system, and finally. This paper concludes with several promising research directions for the future.

A Neural Network in Convolution with Constant Error Carousel Based Long Short Term Memory for Better Face Recognition

Unconstrained face identification, facial periocular recognition, facial land marking and pose prediction, facial expression recognition, 3D facial model design, and other facial-related problems require robust face detection in the wild. Despite the fact that the face recognition issue has been researched intensively for decades with different commercial implementations, it nevertheless faces problems in certain real-world scenarios due to multiple obstacles, such as severe facial occlusions, incredibly low resolutions, intense lighting, exceptionally pose inconsistencies, picture or video compression artefacts, and so on. To solve the problems described above, a face detection technique called Convolution Neural Network with Constant Error Carousel dependent Long Short Term Memory (CNN-CEC-LSTM) is proposed in this paper. This research implemented a novel network structure and designed a special feature extraction that employs a self-channel attention (SCA) block and a self-spatial attention (SSA) block that adaptively aggregates the feature maps in both channel and spatial domains to learn the inter-channel and inter-spatial connection matrices; additionally, matrix multiplications are conducted for a This approach first smoothed the initial image with a Gaussian filter before measuring the gradient image. The Canny-Kirsch Method edge detection algorithm was then used to identify human face edges. The proposed method is evaluated against two recent difficult face detection databases, including the IIT Kanpur Dataset. The experimental findings indicate that the proposed approach outperforms the most current cutting-edge face recognition approaches.

Heart Rate Measurement

In recent studies on non-invasive techniques for heart rate measurements, various Computer Vision algorithms based on Ballistocardiography (BCG) have been employed. This method captures minimal head motions from facial videos, that result from the pumping of blood to the head through the carotid arteries, at each cardiac cycle. We move towards BCG because the conventional technique of Photoplethysmography (PPG) fails to yield accurate results from facial video in case of skin color variations. This paper proposes an improved system for accurately measuring the heart rate and heart rate variability to infer important information about the subject’s health. It incorporates functions from the Dlib toolkit, which provide robust face detection along with facial landmark tracking. Relevant data in terms of facial video and ground truth was acquired from 5 test subjects, in 3 states - sitting, standing and post exercise. The system exhibits promising results when validated using a wearable smart watch with inbuilt heart rate sensor.

Driver fatigue detection based on convolutional neural network and face alignment for edge computing device

Most current vision-based fatigue detection methods don’t have high-performance and robust face detector. They detect driver fatigue using single detection feature and cannot achieve real-time efficiency on edge computing devices. Aimed at solving these problems, this paper proposes a driver fatigue detection system based on convolutional neural network that can run in real-time on edge computing devices. The system firstly uses the proposed face detection network LittleFace to locate the face and classify the face into two states: small yaw angle state “normal” and large yaw angle state “distract.” Secondly, the speed-optimized SDM algorithm is conducted only in the face region of the “normal” state to deal with the problem that the face alignment accuracy decreases at large angle profile, and the “distract” state is used to detect driver distraction. Finally, feature parameters EAR, MAR and head pitch angle are calculated from the obtained landmarks and used to detect driver fatigue respectively. Comprehensive experiments are conducted to evaluate the proposed system and the results show its practicality and superiority. Our face detection network LittleFace can achieve 88.53% mAP on AFLW test set at 58 FPS on the edge computing device Nvidia Jetson Nano. Evaluation results on YawDD, 300 W, and DriverEyes show the average detection accuracy of the proposed system can reach 89.55%.

YOLOv3 Attention Face Detector with High Accuracy and Efficiency

In recent years, face detection has attracted much attention and achieved great progress due to its extensively practical applications in the field of face based computer vision. However, the tradeoff between accuracy and efficiency of the face detectors still needs to be further studied. In this paper, using Darknet-53 as backbone, we propose an improved YOLOv3-attention model by introducing attention mechanism and data augmentation to obtain the robust face detector with high accuracy and efficiency. The attention mechanism is introduced to enhance much higher discrimination of the deep features, and the trick of data augmentation is used in the training procedure to achieve higher detection accuracy without significantly affecting the inference speed. The model has been trained and evaluated on the popular and challenging face detection benchmark, i.e., the WIDER FACE training and validation subsets, respectively, achieving AP of 0.942, 0.919 and 0.821 with the speed of 28FPS. This performance exceeds some existing SOTA algorithms, demonstrating acceptable accuracy and near real time detection for VGA resolution images, even in the complex scenarios. In addition, the proposed model shows good generation ability on another public dataset FDDB. The results indicate the proposed model is a promising face detector with high efficiency and accuracy in the wild.

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.

Feature Extraction for Face Recognition using Edge Detection and Thresholding

Face recognition is first and foremost step in video surveillance applications which include human behavioral analysis, event detection, border security and ATM banking. Most of the time, it is very difficult to get good facial features from the particular image frame and it often requires sophisticated algorithm for face identification and recognition. Robust face detection system is still a more challenging job because of complex environments including illumination changes, background clutter and occlusions. This article presents a novel feature extraction algorithm for face recognition using edge detection and thresholding. Initially, the incoming image is preprocessed to smoothen the image features and it is converted in to grayscale image to reduce the computational complexity of post processing steps. In feature extraction step, the image is completely iterated throughout the spatial coordinates and the edges are detected using thresholding technique. The optimum threshold for global thresholding is identified by calculating the maximum between-class variance in the given image. The extracted edge features are invariant under scale and illumination changes and thus it ensures the robust binary mask for face identification. Finally, the foreground features are obtained using morphological operations and the face is highlighted in subsequent incoming image frames. The proposed method can be deployed in public places such as malls, ATM centers and airports for security applications. Experimental results clearly indicate that the proposed approach works well under complex situations.

Human Emotion Recognition Based on Face and Facial Expression Detection Using Deep Belief Network Under Complicated Backgrounds

The human emotion recognition based on facial expression has a significant meaning in the application of intelligent man–machine interaction. However, the human face images vary largely in real environments due to the complex backgrounds and luminance. To solve this problem, this paper proposes a robust face detection method based on skin color enhancement model and a facial expression recognition algorithm with block principal component analysis (PCA). First, the luminance range of human face image is broadened and the contrast ratio of skin color is strengthened by the homomorphic filter. Second, the skin color enhancement model is established using YCbCr color space components to locate the face area. Third, the feature based on differential horizontal integral projection is extracted from the face. Finally, the block PCA with deep neural network is used to accomplish the facial expression recognition. The experimental results indicate that in the case of weaker illumination and more complicated backgrounds, both the face detection and facial expression recognition can be achieved effectively by the proposed algorithm, meanwhile the mean recognition rate obtained by the facial expression recognition method is improved by 2.7% comparing with the traditional Local Binary Patterns (LBPs) method.

A Systematic Training Procedure for Viola-Jones Face Detector in Heterogeneous Computing Architecture

The face detection has become one of the most important topics in machine learning and computer vision in the last few decades. Many papers have been published utilizing various methods for face detection. One of the most popular face detectors used in many practical applications with heterogeneous computing architecture is Viola-Jones method. Despite of being a real-time and robust face detector, it suffers from not well explained parts at the training procedure, e.g., not clear how to select few features in the first cascades or not clear how many samples are needed and how to gather a good trainset for training a cascade. In this paper, a trainset selection method based on histograms generated from AdaBoost and in addition, simple ways to select few features in beginning cascades are proposed. The training procedure is then compared to a baseline training presented in the previous studies.

Robust Face detection based on Viola-jones Algorithms

This paper describes the experimental methods of using face detection based on MATLAB software. The important things are that interesting in the movement, this face detection are accepted to use four general face detection internationally. We also need to use the Viola and Jones principles in MATLAB for face detection because of its ability to process that image quickly and Very accurate. And this paper aims to study principles and algorithms for active development and improvement.

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.

A multi-agent dynamic system for robust multi-face tracking

The paper presents a new architecture framework in the field of expert and intelligent systems which is based on four paradigms: a novel multi-agent dynamic system architecture (MADS), an extended Belief Desire Intention (EBDI) agent community, autonomy-oriented entities (AOEs), and deep learning concepts. The main impact of the proposed framework is a new approach, or even a new way of thinking, which enables integration of the concepts of deep learning, a conventional approach to solving the domain problem, cognitive agents with mental attitudes, and concepts of nature-inspired computing. All these allow the effective use of the framework in the field of intelligent and expert systems. The significance of the framework lies in its flexibility and adaptability based on the formal logical description of EBDI agents, the definition of the behaviour of AOEs, the use of classical modules for domain problem solving, and modules based on deep-learning concepts. We believe that the example of the adaptation of the proposed architecture framework to robust multi-face tracking illustrates the significance of the proposed framework. In this paper, MADS is adapted to the first two stages of a face de-identification pipeline: robust face detection and multi-face tracking. The proposed architecture of MADS has a two-level hierarchical organization. At the first level there is a manager designed as an Extended Belief Desire Intention (mEBDI) agent. The extension of a manager BDI agent consists of a convolutional neural network-based face detector, a set of autonomous-oriented entities for the elimination of false positive face detections, and a trajectory memory. At the second level, there are many tracking agents (trEBDIs) which consist of a basic BDI agent extended with a face tracker based on position and scale correlation filters, a visual appearance memory, and a trajectory memory. The mEBDI and trEBDI agents are defined by the modal logic and are described at the implementation level. The proposed architecture for a robust multi-face tracking system was tested on a subset of YouTube music videos. The qualitative results, as well as the preliminary quantitative results expressed by the standard testing metrics, demonstrate the effective adaptation of the proposed multi-agent dynamic architecture to a robust multi-face tracking system.

Machine vision quality assessment for robust face detection

Machine vision quality (MVQ) assessment for face detection refers to image quality as judged by face detection algorithms. While perceptual image quality usually depends on human observers, MVQ depends on factors such as the face detection algorithm and performance measures such as recall and precision. We define the MVQ index as a weighted combination of the predicted precision and recall of the face detection algorithm and predict it using image features based on natural scene statistics. A filter bank framework is developed to achieve image enhancement of distorted images for face detection, where the enhancement operations can be optimized for either perceptual quality or MVQ. It is shown that MVQ can be effectively used to control the relative performance of recall and precision that can be achieved on enhanced images. For certain face detection algorithms, optimizing for MVQ instead of perceptual quality can lead to improved face detection performance. The MVQ is developed for three different face detection algorithms and the image enhancement framework is tested on a dataset based on the IDEAL-LIVE Distorted Face Database. A computationally efficient method to optimize for MVQ is designed by predicting the MVQ of enhanced images directly from features extracted from distorted images and filter parameters. The optimization framework is further enhanced by allowing for filter bank independent optimization of MVQ.

Face detection techniques: a review

With the marvelous increase in video and image database there is an incredible need of automatic understanding and examination of information by the intelligent systems as manually it is getting to be plainly distant. Face plays a major role in social intercourse for conveying identity and feelings of a person. Human beings have not tremendous ability to identify different faces than machines. So, automatic face detection system plays an important role in face recognition, facial expression recognition, head-pose estimation, human–computer interaction etc. Face detection is a computer technology that determines the location and size of a human face in a digital image. Face detection has been a standout amongst topics in the computer vision literature. This paper presents a comprehensive survey of various techniques explored for face detection in digital images. Different challenges and applications of face detection are also presented in this paper. At the end, different standard databases for face detection are also given with their features. Furthermore, we organize special discussions on the practical aspects towards the development of a robust face detection system and conclude this paper with several promising directions for future research.

Nighttime Driving Safety Improvement via Image Enhancement for Driver Face Detection

Insufficient illumination makes driver face detection at night challenging. This paper proposes an adaptive attenuation quantification retinex (AAQR) method to enhance the details of nighttime images. There are three phases in this method: attenuation restriction, attenuation prediction, and adaptive quantification. The performance of the proposed method was evaluated by employing a robust face detection method via sparse representation. The collected driver face images at night were categorized into three groups (up-down, left-right, and mixed) according to the illumination distribution in each image. Results have shown that the detection rates of the images enhanced by the proposed AAQR method were 82%, 84%, and 91% for the up-down, left-right, and mixed illumination groups, respectively. In comparison to other image enhancement methods, the detection rate of the AAQR method was 2%–36% greater. Furthermore, the mean computing time for a single $640\times480$ nighttime image using AAQR was less than most of the compared advanced methods. Thus, the AAQR method is recommended for applications in driver face detection tasks at night.

Facial expression recognition based on dual-feature fusion and improved random forest classifier

Facial expression recognition (FER) is an important means for machines to understand the changes in the facial expression of human beings. Expression recognition using single-modal facial images, such as gray scale, may suffer from illumination changes and the lack of detailed expression-related information. In this study, multi-modal facial images, such as facial gray scale, depth, and local binary pattern (LBP), are used to recognize six basic facial expressions, namely, happiness, sadness, anger, disgust, fear, and surprise. Facial depth images are used for robust face detection initially. The deep geometric feature is represented by point displacement and angle variation in facial landmark points with the help of depth information. The local appearance feature, which is obtained by concatenating LBP histograms of expression-prominent patches, is utilized to recognize those expression changes that are difficult to capture by only the geometric changes. Thereafter, an improved random forest classifier based on feature selection is used to recognize different facial expressions. Results of comparative evaluations in benchmarking datasets show that the proposed method outperforms several state-of-the-art FER approaches that are based on hand-crafted features. The capability of the proposed method is comparable to that of the popular convolutional neural-network-based FER approach but with fewer demands for training data and a high-performance hardware platform.

Personalized food printing for portrait images

The recent development of 3D printing techniques enables novel applications in customized food fabrication. Based on a tailor-made 3D food printer, we present a novel personalized food printing framework driven by portrait images. Unlike common 3D printers equipped with materials such as ABS, Nylon and SLA, our printer utilizes edible materials such as maltose, chocolate syrup, jam to print customized patterns. Our framework automatically converts an arbitrary input image into an optimized printable path to facilitate food printing, while preserving the prominent features of the image. This is achieved based on two key stages. First, we apply image abstraction techniques to extract salient image features. Robust face detection and sketch synthesis are optionally involved to enhance face features for portrait images. Second, we present a novel path optimization algorithm to generate printing path for efficient and feature-preserving food printing. We demonstrate the efficiency and efficacy of our framework using a variety of images and also a comparison with non-optimized results.