Haar Feature-based Cascade Classifier Research Articles

Investigation and Application of Various Algorithms used in Object Detection and Classification in Image Data

In this article, we compare and analyze the YOLO (You Only Look Once) method, widely employed for object detection in digital image processing, with the Haar feature-based cascade classifier method implemented using the OpenCV library. YOLO, a deep learning–based approach, excels in real-time object detection and recognition applications. In contrast, the Haar method utilizes a traditional approach to rapidly identify features. However, significant performance differences exist between the two methods. Experimental results and performance analyses demonstrate that YOLO provides high accuracy rates and real-time processing speeds in object detection tasks. The code implementations presented in this study will be valuable to researchers new to digital image processing. Additionally, YOLO has shown high performance on large and complex datasets by leveraging GPU capabilities. Experiments with various YOLO versions (e.g., YOLOv4, YOLOv5, YOLOv7) have established it as one of the most suitable options for real-time applications, particularly due to its low latency and high accuracy.

Enhancing Public Safety: A Comprehensive Analysis of Crime Vision Strategies

There is an abnormal increase in the crime rate and also the number of criminals are increasing, this leads towards a great concern about the security issues. Crime preventions and criminal identification are the primary issues before the police personnel, since property and lives protection are the basic concerns of the police but to combat the crime, the availability of police personnel is limited. With the advent of security technology, cameras especially CCTV have been installed in many public and private areas to provide surveillance activities. The footage of the CCTV can be used to identify suspects on scene. This Real time criminal identification system based on face recognition works with a fully automated facial recognition system. HAAR feature-based cascade classifier and OpenCV LBPH (Local Binary Pattern Histograms) Algorithms are used for Face detection and recognition. This system will be able to detect face and recognize face automatically in real time. An accurate location of the face is still a challenging task. This Framework has been widely used by researchers in order to detect the location of faces and objects in a given image. Face detection classifiers are shared by public communities, such as OpenCV. The automatically tagging feature adds a new dimension to sharing pictures among the people who are in the picture and also gives the idea to other people about who the person is in the image. In our project, we have studied and implemented a pretty simple but very effective face detection using CNN algorithm which takes human skin color into account. Our aim, which we believe we have reached, was to develop a system that can be used by police or investigation department to recognize criminal from their faces. The method of face recognition used is fast, robust, reasonably simple and accurate with a relatively simple and easy to understand algorithms and technique.

Track A Missing Child Using Local Binary Pattern Histogram Based Face Recognition Method

In our society, a countless number of children are missing every-day. Even though missing cases are getting filed against them in nearby police stations but it is complicated to trace them through investigation as it requires lot of efforts and it is time consuming also it becomes quite challenging for police to find them and it’s really getting impossible to find them in most cases. So to make it convenient we planned to design a website for “Tracking the missing child using Face Recognition ”, which makes the task quite simpler. The main intention of this project is to reunite the missing children with their parents, so we are going to design a web page where one can be able to upload the missing child details and store it in database. If the child who is missing is identified on road by any random person, then he/she will upload the photo of that child on web page then our system can be able to detect that lost child using face recognition algorithms. Whenever the system confirms the matching then it will set up custom alerts along with location and send them directly to family members and concerned investigation officers . And if the match is not found , then the person will be provided with the option of registering child photo as a new entry to our database with the location they found. So, in this way we can find the lost children in more easy and convenient way. In this model, an automated facial recognition system for Missing Child database was proposed using known Haar feature-based cascade classifier. This system will be able to detect face and recognize face automatically in real time. In our model we are going to use Face Recognition module of python.

DIGITAL IMAGES CLASSIFICATION IN AUTOMATIC LAPAROSCOPIC DIAGNOSTICS

The aim: To evaluate the automatic computer diagnostic (ACD) systems, which were developed, based on two classifiers-HAAR features cascade and AdaBoost for the laparoscopic diagnostics of appendicitis and ovarian cysts in women with chronic pelvic pain. Materials and methods: The training of HAAR features cascade, and AdaBoost classifiers were performed with images/ frames of laparoscopic diagnostics. Both gamma-corrected RGB and RGB converted into HSV frames were used for training. Descriptors were extracted from images with the method of Local Binary Pattern (LBP), which includes both data on color characteristics («modified color LBP»-MCLBP) and textural features. Results: Classification of test video images revealed that the highest recall for appendicitis diagnostics was achieved after training of AdaBoost with MCLBP descriptors extracted from RGB images - 0.708, and in the case of ovarian cysts diagnostics - for MCLBP gained from RGB images - 0.886 (P<0.05). Developed AdaBoost-based ACD system achieved a 73.6% correct classification rate (accuracy) for appendicitis and 85.4% for ovarian cysts. The accuracy of the HAAR features classifier was highest in the case of ovarian cysts identification and achieved 0,653 (RGB) - 0,708 (HSV) values (P<0.05). Conclusions: The HAAR feature-based cascade classifier turned out to be less e"ective when compared with the AdaBoost classifier trained with MCLBP descriptors. Ovarian cysts were better diagnosed when compared with appendicitis with the developed ACD.

AI BASED REALTIME FACE MASK DETECTION

The world has not yet fully Recover from this pandemic and the vaccine that can effectively treat Covid-19 is yet to be discovered. Wearing a face mask will help prevent the spread of infection and prevent the individual from contracting any airborne infectious germs. Done by evaluation of the classification results by analysing real-time streaming from the Camera. Using Face Mask Detection System, one can monitor if the people are wearing masks or not. Here HAARCASACADE algorithm is used for image detection. Collating with other existing algorithms, this classifier produces a high recognition rate even with varying expressions, efficient feature selection and low assortment of false positive features. HAAR feature-based cascade classifier system utilizes only 200 features out of 6000 features to yield a recognition rate of 98-100%. According to this motivation we demand mask detection as a unique and public health service system during the global pandemic COVID-19 epidemic. The model is trained by face mask image and non-face mask image. KEYWORDS Face mask detection Face edges (FMD) CNN- Convolutional Neural Network

Masked face with facial expression recognition based on deep learning

Wearing masks contributed to slowing the spread of the coronavirus disease (COVID-19) as the World Health Organization (WHO) recommended wearing face masks especially with the spreading of virus variants like omicron. Although people accept the idea of wearing these masks, it is still unknown the effect of covering parts of the face on social interaction among people in general and children in particular. Moreover, Social isolation affects emotional moods, which causes stress, sadness, and depression. In the current study, we have been exploring the emotional inferences on faces with and without a mask. The system can pick up the universal emotions: fear, disgust, anger, surprise, contempt, sadness, and happiness. The researchers in deep learning are concerned with global pandemic COVID-19 to enhance public health service. The proposed model is developed with a machine learning algorithm through the Haar feature-based cascade classifiers. The built model can detect people's emotions with mask and without a mask with high accuracy.

Detecting potholes on Indian roads using Haar feature-based cascade classifier, convolutional neural network, and instance segmentation

Detecting potholes on Indian roads using Haar feature-based cascade classifier, convolutional neural network, and instance segmentation

Machine learning with different digital images classification in laparoscopic surgery

The evaluation of the effectiveness of the automatic computer diagnostic (ACD) systems developed based on two classifiers – HAAR features cascade and AdaBoost for the laparoscopic diagnostics of appendicitis and ovarian cysts in women with chronic pelvic pain is presented. The training of HAAR features cascade, and AdaBoost classifiers were performed with images/ frames, which have been extracted from video gained in laparoscopic diagnostics. Both gamma-corrected RGB and RGB converted into HSV frames were used for training. Descriptors were extracted from images with the method of Local Binary Pattern (LBP), which includes both data on color characteristics («modified color LBP» - MCLBP) and textural characteristics, which have been used later on for AdaBoost classifier training. Classification of test video images revealed that the highest recall for appendicitis diagnostics was achieved after training of AdaBoost with MCLBP descriptors extracted from RGB images – 0.708, and in the case of ovarian cysts diagnostics – for MCLBP gained from RGB images – 0.886.&#x0D; Developed AdaBoost-based ACD system achieved a 73.6% correct classification rate (accuracy) for appendicitis and 85.4% for ovarian cysts. The accuracy of the HAAR features classifier was highest in the case of ovarian cysts identification and achieved 0.653 (RGB) – 0.708 (HSV) values. It was concluded that the HAAR feature-based cascade classifier turned to be less effective when compared with the AdaBoost classifier trained with MCLBP descriptors. Ovarian cysts were better diagnosed when compared with appendicitis with the developed ACD.

A Dynamic Density Based Traffic Control System Using IoT

In this paper, a system to regulate the traffic with the help of real-time vehicle density using Haar feature-based cascade classifier is proposed. Based on the traffic densities on all roads, the model allocates smartly the duration for which the green light glows. Image processing has been chosen for calculation of traffic density as cameras are very much cheaper than other devices such as sensors. The proposed model is constructed as follows: A Raspberry Pi that is connected to 4 sets of LEDs that represent the traffic lights is used. It does the process of monitoring the traffic density of each side and changes the signal according to the density in every direction. Therefore the side with higher traffic density will have green signal turned on for a longer duration. Thus it makes the traffic signaling system automatic, by providing the vehicle density measurement to the system. It makes sure that there are no difficulties in traffic handling.

An adaptive pig face recognition approach using Convolutional Neural Networks

The evolution of agriculture towards intensive farming leads to an increasing demand for animal identification associated with high traceability, driven by the need for quality control and welfare management in agricultural animals. Automatic identification of individual animals is an important step to achieve individualised care in terms of disease detection and control, and improvement of the food quality. For example, as feeding patterns can differ amongst pigs in the same pen, even in homogenous groups, automatic registration shows the most potential when applied to an individual pig. In the EU for instance, this capability is required for certification purposes. Although the RFID technology has been gradually developed and widely applied for this task, chip implanting might still be time-consuming and costly for current practical applications. In this paper, a novel framework composed of computer vision algorithms, machine learning and deep learning techniques is proposed to offer a relatively low-cost and scalable solution of pig recognition. Firstly, pig faces and eyes are detected automatically by two Haar feature-based cascade classifiers and one shallow convolutional neural network to extra high-quality images. Secondly, face recognition is performed by employing a deep convolutional neural network. Additionally, class activation maps generated by grad-CAM and saliency maps are utilised to visually understand how the discriminating parameters have been learned by the neural network. By applying the proposed approach on 10 randomly selected pigs filmed in farm condition, the proposed method demonstrates the superior performance against the state-of-art method with an accuracy of 83% over 320 testing images. The outcome of this study will facilitate the real-application of AI-based animal identification in swine production.

Biometric for Cattle Identification using Muzzle Patterns

Similar to human biometrics such as faces and fingerprints, animals also have biometrics for individual identifiers. This research paper works on biometrics of cattle using images of muzzle patterns. The proposed approach begins with a training process to construct a cattle face localization model using a Haar feature-based cascade classifier. Then, the watershed technique is applied to segment a region of interest (RoI) of a muzzle area in the detected region of the cattle face. This muzzle ROI is further enhanced to make ridge lines more outstanding. The next step, using two approaches, is to extract a main feature descriptor based on a bag of histograms of oriented gradients (BoHoG) and a histogram of local binary patterns (LBP). Then, the support vector machine (SVM) is applied with the histogram intersection kernel for a final cattle identifier. The proposed method is evaluated using five different datasets including one existing cattle dataset used in previous research works, one newly collected dataset of swamp buffalo captured in a controlled environment, and three newly collected datasets of swamp buffalo captured in an outdoor field environment. This outdoor field environment includes challenges of freely moving cattle and differences in daylight. It could achieve a promising accuracy of 95% for a large dataset of 431 subjects.

A Novel Changing Athlete Body Real-Time Visual Tracking Algorithm Based on Distractor-Aware SiamRPN and HOG-SVM

Athlete detection in sports videos is a challenging task due to the dynamic and cluttered background. Distractor-aware SiamRPN (DaSiamRPN) has a simple network structure and can be utilized to perform long-term tracking of large data sets. However, similarly to the Siamese network, the tracking results heavily rely on the given position in the initial frame. Hence, there is a lack of solutions for some complex tracking scenarios, such as running and changing of bodies of athletes, especially in the stage from squatting to standing to running. The Haar feature-based cascade classifier is involved to catch the key frame, representing the video frame of the most dramatic changes of the athletes. DaSiamRPN is implemented as the tracking method. In each frame after the key frame, a detection window is given based on the bounding box generated by the DaSiamRPN tracker. In the new detection window, a fusion method (HOG-SVM) combining features of Histograms of Oriented Gradients (HOG) and a linear Support-Vector Machine (SVM) is proposed for detecting the athlete, and the tracking results are updated in real-time by fusing the tracking results of DaSiamRPN and HOG-SVM. Our proposed method has reached a stable and accurate tracking effect in testing on men’s 100 m video sequences and has realized real-time operation.

Towards the automation of early-stage human embryo development detection

BackgroundInfertility and subfertility affect a significant proportion of humanity. Assisted reproductive technology has been proven capable of alleviating infertility issues. In vitro fertilisation is one such option whose success is highly dependent on the selection of a high-quality embryo for transfer. This is typically done manually by analysing embryos under a microscope. However, evidence has shown that the success rate of manual selection remains low. The use of new incubators with integrated time-lapse imaging system is providing new possibilities for embryo assessment. As such, we address this problem by proposing an approach based on deep learning for automated embryo quality evaluation through the analysis of time-lapse images. Automatic embryo detection is complicated by the topological changes of a tracked object. Moreover, the algorithm should process a large number of image files of different qualities in a reasonable amount of time.MethodsWe propose an automated approach to detect human embryo development stages during incubation and to highlight embryos with abnormal behaviour by focusing on five different stages. This method encompasses two major steps. First, the location of an embryo in the image is detected by employing a Haar feature-based cascade classifier and leveraging the radiating lines. Then, a multi-class prediction model is developed to identify a total cell number in the embryo using the technique of deep learning.ResultsThe experimental results demonstrate that the proposed method achieves an accuracy of at least 90% in the detection of embryo location. The implemented deep learning approach to identify the early stages of embryo development resulted in an overall accuracy of over 92% using the selected architectures of convolutional neural networks. The most problematic stage was the 3-cell stage, presumably due to its short duration during development.ConclusionThis research contributes to the field by proposing a model to automate the monitoring of early-stage human embryo development. Unlike in other imaging fields, only a few published attempts have involved leveraging deep learning in this field. Therefore, the approach presented in this study could be used in the creation of novel algorithms integrated into the assisted reproductive technology used by embryologists.

Traffic Analysis and Signal Timer Management Using OpenCV

Traffic has been a major issue at intersections throughout the world, here a technique is proposed to reduce manpower required to handle the traffic and remove static timers from intersections. Proposed system is consisting of simple computer device with CCTV. It works by analyse traffic condition from video input and then count vehicles to manipulate signal timer which avoid traffic collision and maintain traffic flow. Therefor the first step in this process is taking video input from mounted CCTV camera and then detection of cars. The system uses Haar like features, which is mainly made for face detection. Haar feature-based cascade classifier is effective object detection technique. It has data set containing positive and negative image data which help agent to identify target object. Result shows this system is more effective in detection of cars compare to existing systems.

Learning combined features for automatic facial expression recognition

Facial expressions are one of the most natural and powerful means for the human being in his social communications, whether to share his internal emotional states or to display his moods or intentions, which, in fact, may be true or simply played in a theatrical way. Given the numerous and variety of applications that can be easily planned, building a system able to automatically recognising facial expressions from images has been an intense field of study in recent years. In this paper, we propose a new framework for automatic facial expression recognition based on combined features and deep learning method. Before the feature extraction, we use Haar feature-based cascade classifier in order to detect then crop the face in the images. Next, we extract pyramid of histogram of gradients (PHOG) as shape descriptors and local binary patterns (LBP) as appearance features to form hybrid feature vectors. Finally, we use those vectors for training deep learning algorithm called deep belief network (DBN). The experimental results on publicly available datasets show promising accuracy in recognising all expression classes, even for experiments which are evaluated on more than seven basic expressions.

Learning combined features for automatic facial expression recognition

Facial expressions are one of the most natural and powerful means for the human being in his social communications, whether to share his internal emotional states or to display his moods or intentions, which, in fact, may be true or simply played in a theatrical way. Given the numerous and variety of applications that can be easily planned, building a system able to automatically recognising facial expressions from images has been an intense field of study in recent years. In this paper, we propose a new framework for automatic facial expression recognition based on combined features and deep learning method. Before the feature extraction, we use Haar feature-based cascade classifier in order to detect then crop the face in the images. Next, we extract pyramid of histogram of gradients (PHOG) as shape descriptors and local binary patterns (LBP) as appearance features to form hybrid feature vectors. Finally, we use those vectors for training deep learning algorithm called deep belief network (DBN). The experimental results on publicly available datasets show promising accuracy in recognising all expression classes, even for experiments which are evaluated on more than seven basic expressions.

Robust techniques for abandoned and removed object detection based on Markov random field

This paper presents a novel framework for detecting abandoned objects by introducing a fully-automatic GrabCut object segmentation. GrabCut seed initialization is treated as a background (BG) modelling problem that focuses only on unhanded objects and objects that become immobile. The BG distribution is constructed with dual Gaussian mixtures that are comprised of high and low learning rate models. We propose a primitive BG model-based removed object validation and Haar feature-based cascade classifier for still-people detection once a candidate for a released object has been detected. Our system can obtain more robust and accurate results for real environments based on evaluations of realistic scenes from CAVIAR, PETS2006, CDnet 2014, and our own datasets.

Region-Based Facial Expression Recognition in Still Images

In Facial Expression Recognition Systems (FERS), only particular regions of the face are utilized for discrimination. The areas of the eyes, eyebrows, nose, and mouth are the most important features in any FERS. Applying facial features descriptors such as the local binary pattern (LBP) on such areas results in an effective and efficient FERS. In this paper, we propose an automatic facial expression recognition system. Unlike other systems, it detects and extracts the informative and discriminant regions of the face (i.e., eyes, nose, and mouth areas) using Haar-feature based cascade classifiers and these region-based features are stored into separate image files as a preprocessing step. Then, LBP is applied to these image files for facial texture representation and a feature-vector per subject is obtained by concatenating the resulting LBP histograms of the decomposed region-based features. The one-vs.-rest SVM, which is a popular multi-classification method, is employed with the Radial Basis Function (RBF) for facial expression classification. Experimental results show that this approach yields good performance for both frontal and near-frontal facial images in terms of accuracy and time complexity. Cohn-Kanade and JAFFE, which are benchmark facial expression datasets, are used to evaluate this approach.