Face Detection Using Haar Cascades Classifier (original) (raw)
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Face Detection Using OpenCV and Haar Cascades Classifiers
M.Sc (Data Science and Analytics) project presentation, 2020
This project used the OpenCV library for face detection, eye detection, and nose detection in a given color image. Haar Cascade Classifier has been used for doing the tasks. For training the model with the feature set of a face, we used the “Haar frontal face” XML file. We later extended our model to detect eyes and nose in the same input image. We used “haar_eyes” and “haar_mcs_nose” XML files for this purpose. Our model could successfully detect all faces, eyes, and noses in the input image with 100% detection accuracy and with real-time detection speed.
Detection of Faces from Images Using Haar Cascade Classifier
Iconic Research and Engineering Journals, 2020
Nowadays, the increasing volume of images is absolutely demanded in most of digital image processing and pattern recognition. Moreover, face detection from images has become essential as it can be applied in various areas such as surveillance system, biometrics, gender classification, and so on. In this paper, Haar Cascade Classifier of Open Source Computer Vision Library (OpenCV) is utilized in detection of faces. In addition, Apache Hadoop, a distributed processing platform is applied to solve the computation time burden of face detection from large-scale images. According to the experimentation, the face detection with haar cascade classifier which is experimented on Apache Hadoop platform can offer satisfactory execution time results.
Evaluation Of Haar Cascade Classifiers Designed For Face Detection
2012
In the past years a lot of effort has been made in the field of face detection. The human face contains important features that can be used by vision-based automated systems in order to identify and recognize individuals. Face location, the primary step of the vision-based automated systems, finds the face area in the input image. An accurate location of the face is still a challenging task. Viola-Jones 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. An evaluation of these classifiers will help researchers to choose the best classifier for their particular need. This work focuses of the evaluation of face detection classifiers minding facial landmarks.
Face Detection Pada Gambar Dengan Menggunakan Opencv Haar Cascade
INTI Nusa Mandiri
OpenCV has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. It has been proven by software companies, that is why the researcher will use it for face detection application with Java programming langguage. The purpose of this paper is trying to implement machine learning library OpenCV with Haarcascade algorithm to detect face from an image which have five variations, which are; viewpoint variations, illumination variations, facial expression, and occlusions. Haar cascade is proven have drawback in detecting such image, but this can be fixed by optimizing the false negative model.
Human face detection has been a challenging issue in the areas of image processing and patter recognition. A new human face detection algorithm by primitive Haar cascade algorithm combined with three additional weak classifiers is proposed in this paper. The three weak classifiers are based on skin hue histogram matching, eyes detection and mouth detection. First, images of people are processed by a primitive Haar cascade classifier, nearly without wrong human face rejection (very low rate of false negative) but with some wrong acceptance (false positive). Secondly, to get rid of these wrongly accepted non-human faces, a weak classifier based on face skin hue histogram matching is applied and a majority of non-human faces are removed. Next, another weak classifier based on eyes detection is appended and some residual non-human faces are determined and rejected. Finally, a mouth detection operation is utilized to the remaining non-human faces and the false positive rate is further decreased. With the help of OpenCV, test results on images of people under different occlusions and illuminations and some degree of orientations and rotations, in both training set and test set show that the proposed algorithm is effective and achieves state-of-the-art performance. Furthermore, it is efficient because of its easiness and simplicity of implementation.
Building Custom HAAR-Cascade Classifier for face Detection
International Journal of Engineering Research & Technology (IJERT), 2019
There are superior pre-trained HAAR-Cascade classifiers available on the Internet whose detection accuracy is quite impressive for the task of face detection in the presence of different illuminations conditions and different poses of the face. But the drawback to using such pre-trained classifies for any detection task is we never know how training of such classifiers can be done, how to prepare the dataset for a particular detection task and how to use different parameters of the classifiers while training. In this paper, we build our own Custom HAAR-Cascade Classifier using "Cascade Trainer GUI (a tool designed by Amin Ahmadi) to detect face/faces in any given image/images. We also create a dataset which includes positive and negative samples to use during training purpose. We also demonstrate how to retrain the classifier after analyzing the error matrix after each detection stage and how to increase the accuracy of the classifier in detection work.
Automatic Face Recognition and Detection Using OpenCV, Haar Cascade and Recognizer for Frontal Face
This research is based on real-time automatic frontal face recognition and detection using OpenCV, Haar Cascade and recognizers. The recognizers used are Eigenface, Fisherface and LBPH with Haar cascade. These algorithms are firstly trained with images stored in database and then the testing is done using real-time images captured through camera. The results have been compared based on accuracy of recognition rate. It is found that if we increase the distance between person and camera the Eigenface cannot detect and recognize the person properly. On other hand the LBPH and Fisherface gave best work performance withcapability to detect and recognize the authorized person with ±5% tilt angle and varyingfacial expressionsin both normal light condition (day) and low light conditions (night).
—Face detection is an interesting area in research application of computer vision and pattern recognition, especially during the past several years. It is also plays a vital role in surveillance systems which is the first steps in face recognition systems. The high degree of variation in the appearance of human faces causes the face detection as a complex problem in computer vision. The face detection systems aimed to decrease false positive rate and increase the accuracy of detecting face especially in complex background images. The main aim of this paper is to present an up-to-date review of face detection methods including feature-based, appearance-based, knowledge-based and template matching. Also, the study presents the effect of applying Haar-like features along with neural networks. We also conclude this paper with some discussions on how the work can be taken further. Keywords—face detection; feature based face detection; human face detection; haar-like features; neural networks.
Implementation of Face Detection System Using Haar Classifiers
cerc.wvu.edu
This paper presents a hardware implementation of face and eyes detection algorithm. To become we must go through several stages. First we have implemented an algorithm for detecting and tracking eyes written in C using an open source library of image processing and computer vision the "OpenCV". Then a profiling on HW/SW (hardware/software) partition was done. The hardware part solves the part of the algorithm with higher computational costs. The system has been implemented on Spartan3A DSP FPGA board. In order to visualize the results on real images, we have made a co-simulation of this bloc using the Simulink tool of Matlab.
The performance of the haar cascade classifiers applied to the face and eyes detection
Computer Recognition Systems 2, 2007
Recently we have presented the hierarchical face and eye detection system based on Haar Cascade Classifiers. In this paper we focus on the optimization of detectors training. Moreover, we compare the performance of Lienhart's face detectors [1] and Castrillon-Santana's eyes detectors [2] with those which have been trained by us.