Haar Cascade Classifier Research Articles

Improved AdaBoost Algorithm for Face Detection and Its Application

Face detection plays a crucial role in developing human-robot interaction (HRI) for Intelligent Educational Robot (IER) to recognize users or speakers. In this paper, we introduce an intelligent vision algorithm that is able to detect human face from complex scene and filter out all the non-face but face-like images. The human face is detected in real time using the approach called AdaBoost-based Haar-Cascade Classifier[1-2], and the real human face detection is improved to implement from single-face detection to multi-face detection. Furthermore, variable head pose is taken into account, such as pitch, roll, yaw, etc. The proposed robot vision algorithm for human detection is tested to be effective and robust through real-time experiments.

Embedded design and implementation of face detection and face recognition system with haar cascade classifier and eigenface algorithm

Embedded design and implementation of face detection and face recognition system with haar cascade classifier and eigenface algorithm

Conversion of Tactile Sign Language into English for Deaf/Dumb Interaction

Natural language is the way of communication for normal human beings which includes spoken language, written and body gestures i.e. head gesture, hand gestures, facial expressions and lip motion etc. On the other hand, speech and hearing-impaired people uses sign language for communication which is not understandable for normal people thus they face problems of communication in society. In this problem interpreters are required but the human interpreters are costly and are not an efficient solution. Thus, there is a need of system which will translate the sign language into normal language which will be understandable by normal. The system proposed and explained in the paper is an efficient solution to this problem. In the system, sign recognition is done using CAMSHIFT and P2DHHM algorithm followed by Haar Cascade Classifier. After sign recognition, the language technology techniques of POS tagging and LALR parser are used to convert recognized sign words into English sentence. Till date no any system has worked on sentence framing. Results shows that this system produces 92% of accurate result which will bridge the gap between impaired and Normal people.

Triah: an intelligent guiding system for the visually impaired

Computer vision can be deemed as an emerging field which processes real world images in order to display a variety of numerical information about them. To improve the quality of the lifestyle that the visually impaired possess, we propose an assistive model which combines the various aspects of computer vision. Our proposed model aims at detecting the number of faces by using haar cascade classifiers and integrating it with Raspberry Pi. The processed images are run through the classifier and sum total is alerted through an auditory output via headphones. This model further boasts an average accuracy of 82.5%. Our model also notifies the visually impaired of the spatial orientation of the people surrounding them.

Proposing a Novel Three Triangles Geometrical Approach for Face Detection on Occluded Facial Features

This paper is associated with the novel approach for detecting the face and facial features from the images where the facial features are occluded. Face detection is one of the developing trends in the field of biometrics. A lot of face detection algorithm has been developed, but still the face detection rate haven’t reached to 100%. It is due to the presence of various environmental conditions on an image. The occluded facial image is also one of the conditions for high performance face detection. A face is occluded if some area of the face is hidden behind on an object like a mask, sunglass, hand or we can say the face is partially visible. In this paper, we have developed a system, that detects the presence of the facial features and the face from the still images even the facial components are occluded. In this system, we calculate the geometrical position of the facial components by using the facial geometry by calculating the distance and the location of the facial features and the width of the face. The system first extracts the eyes, nose and mouth also known as the facial features by using the skin color model and the Haar cascades. The geometrical model is then applied to the extracted features using the three triangle method that calculates the height, width, distance and the position of the extracted features. Our proposed face detector then detects the presence of the face in an image even with an occlusion. We are developing the face detector for still film image by combining the “three triangle method” with “UKF” and “Haar cascade classifier”, which represents the novelty of our proposed system. We performed our experiments on various still film images under di ff erent conditions, using di ff erent facial databases to clarify the e ff ectiveness of our proposed method.

Improvement of Sample Selection: A Cascade-Based Approach for Lesion Automatic Detection

Computer-Aided Detection (CADe) system has a significant role as a preventative effort in the early detection of breast cancer. There are some phases in developing the pattern recognition on the CADe system, including the availability of a large number of data, feature extraction, selection and use of features, and the selection of the appropriate classification method. Haar cascade classifier has been successfully developed to detect the faces in the multimedia image automatically and quickly. The success of the face detection system must not be separated from the availability of the training data in the large numbers. However, it is not easy to implement on a medical image because of some reasons, including its low quality, the very little gray-value differences, and the limited number of the patches for the examples of the positive data. Therefore, this research proposes an algorithm to overcome the limitation of the number of patches on the region of interest to detect whether the lesion exists or not on the mammogram images based on the Haar cascade classifier. This research uses the mammogram and ultrasonography images from the breast imaging of 60 probands and patients in the Clinic of Oncology, Yogyakarta. The testing of the CADe system is done by comparing the reading result of that system with the mammography reading result validated with the reading of the ultrasonography image by the Radiologist. The testing result of the k-fold cross validation demonstrates that the use of the algorithm for the multiplication of intersection rectangle may improve the system performance with accuracy, sensitivity, and specificity of 76%, 89%, and 63%, respectively.

Monitor Student’s Presence in Classroom

The real time face detection and recognition is now a days a subject of interest in various daily applications like crowd identification, video conference, security measure, image analysis etc. This topic has brought attention of researchers because the human face is a dynamic object and has a high degree of variability in their appearances, which make face detection a difficult problem in computer vision. Many technique are being proposed, ranging from simple edge based algorithm to composite high level approaches utilizing advanced pattern recognition methods. The algorithms presented in this paper are Viola-Jones algorithm (Haar Cascade Classifier) and PCA (classified as either feature based and image based) and are discussed in terms of technical approach and performance. The objective of this paper is to find out a way to monitor student’s presence in classroom using EmguCV (Computer vision Library and wrapper class of Open CV) and send important notification to parents to keep track of their child from remote location. This technology used to monitor their children and by this way teachers are trying to guard children’s behaviors in order to reduce teachers’ difficulties of classroom management, instead of helping children to learn effectively.

Pengolahan Citra Digital untuk Keyboard Virtual Sebagai Antarmuka pada Aplikasi Berbasis Web

AbstrakSejak pertama kali komputer ditemukan, keyboard selalu menjadi alat utama yang menjadi penghubung interaksi antara manusia dan komputer. Saat ini banyak komputer yang menerapkan teknologi pengolahan citra untuk menjadikannya perantara interaksi antara komputer dan manusia.Dalam penelitian ini, penulis mencoba untuk menerapkan teknologi pengolahan citra yang digunakan untuk keyboard virtual pada aplikasi web. Digunakan webcam untuk menangkap citra ujung jari telunjuk. Hasil capture citra akan dikirimkan ke server localhost untuk diproses dengan image processing. Untuk mendeteksi ujung jari telunjuk, digunakan metode Haar Cascade Classifier. Proses pendeteksian tersebut menghasilkan koordinat yang akan dikirimkan ke aplikasi web yang selanjutnya dijadikan acuan untuk menentukan posisi tombol pada keyboard virtual. Sehingga keyboard virtual akan menampilan karakter sesuai dengan yang ditunjuk oleh ujung jari telunjuk.Dari hasil pengujian yang dilakukan, jarak optimal ujung jari telunjuk dengan webcam adalah 20 – 35 cm. Derajat kemiringan ujung jari telunjuk untuk dapat terdeteksi antara 0° – 10°. Sistem mampu mengenali ujung jari telunjuk pada ruangan berlatar belakang putih polos dan terdapat sedikit perabot. Waktu respon untuk menampilkan karakter keyboard virtual rata-rata 5,156 detik. Sehingga keyboard virtual pada sistem ini belum mampu dijadikan antarmuka pada aplikasi web, dikarenakan masih sulit digunakan dalam mengarahkan ujung jari telunjuk ke tombol karakter yang diinginkan.Kata kunci—aplikasi web, Haar Cascade Classifier, keyboard virtual, pengolahan citra AbstractSince the first computer was founded, keyboard is always been a primary tool for interaction between humans and computers. Today, many computers use image processing technology to make interaction between computers and humans.The author try to apply image processing technology that implemented to virtual keyboard on web application. Using a webcam to capture the tip of index finger and the results will be sent to the localhost server for processing with image processing. Using Haar Cascade Classifier method to detect the tip of index finger, it will produce coordinates that sent to the web application and it used as a reference for determining button positions on virtual keyboard. Virtual keyboard characters will display after appointed by the tip of index finger.From testing results, optimal distance from index finger to webcam is 20 – 35 cm. System can recognize the tip of index finger on white background and room with few furnitures. Average response time for displaying virtual keyboard sentences is 3 minutes and 28.838 seconds. So the virtual keyboard on this system was not able to be used as interface on web application, because it difficult to use in directing the tip of index finger to the character keys.Keywords—web application, Haar Cascade Classifier, virtual keyboard, image processing

Arabic character recognition using a Haar cascade classifier approach (HCC)

Optical character recognition (OCR) shows great potential for rapid data entry, but has limited success when applied to the Arabic language. Traditional OCR problems are compounded by the nature of Arabic language and because the script is heavily connected. A machine learning, Haar cascade classifier (HCC) approach was introduced by Viola and Jones (Rapid object detection using a boosted cascade of simple features. Kauai, Hawaii, 2001) to achieve rapid object detection based on a boosted cascade of simple Haar-like features. Here, that approach is applied for the first time to suit Arabic glyph recognition. HCC approach eliminates problematic steps in the pre-processing and recognition phases and, most importantly, character segmentation stage. A classifier was produced for each of the 61 Arabic glyphs that exist after the removal of diacritical marks (dots). These classifiers were trained and tested on some 2,000 images each. The system was tested with real text images and produces a recognition rate for Arabic glyphs of 87 %. The technique gives good results relative to those achieved using a commercial Arabic OCR application and existing state-of-the-art research application.

Object Detection for Markerless Augmentation using Haar Training

Computer Vision is a field that includes methods for acquiring, processing, analyzing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information etc[1]. As a highly scientific field, computer vision is concerned with the theory behind artificial systems that extract information from images involving image processing and object recognition. In our research we aim to explore this extensive domain of object recognition and image processing in terms of real time environment and deploy its application on various open source environments like Android. For achieving this we have incorporated a recently introduced Viola Jones, rapid object detection scheme based on a boosted cascade of simple features. For the object detection and recognition, we performed Haar Training on a set of positive and negative samples, which result in creation of Haar Cascade Classifier files in xml formats. These files are then used for detection. Post successful completion, the proposed project could also find its application in Markerless Augmented Reality as recent convergence of imaging sensors and general purpose processors on mobile phones creates an opportunity for a new class of augmented reality applications. Keywords: Computer Vision, Object Recognition, Viola Jones, Android, Haar Training, Haar Cascade Classifier, Markerless Augmented Reality

A Method for Selecting the Most Informative Iris Image from Real Time Video Stream

본 논문에서는 영상 캡쳐 기기를 통해 입력되는 비디오 스트림으로부터 개인 인증에 가장 유용한 홍채 영상을 선택하는 방법을 제안한다. 첫째, Haar 직렬 분류기를 통하여 눈의 존재여부를 확인한다. 이 단계에서는 데이터 획득이 이루어질 때, 승인된 영상이 눈 영상임을 사용자의 협조 없이도 확인한다. 다음 눈 영역에 대한 탐지를 시행하고, 동공을 추적한다. 최종적으로 두 번째 단계에서 얻어진 파라미터를 이용하여 가장 유용한 홍채 영상을 선택한다. 본 방법은 기존의 복잡하고 하드웨어적 보조가 필요한 방법들에 비교할만하다. 적절한 계산 속도와 높은 탐지율을 보임으로써, 본 방법은 실시간 홍채인식 시스템들이 데이터 획득시 효과적으로 동작하도록 할 것이다.

An ARM based Door Phone Embedded System for Voice and Face Identification and Verification by Open CV and Qt GUI Framework

phone embedded system for voice and face based user recognition is mainly used for security purpose. A natural way to identify a person is through their voice and face. Voice characteristics are different among individuals due to differences in their sound dynamics, vocal chords etc. A practical problem is voice can be trap by anyone. This proposed system is to recognize user password through voice and user recognize face at the door based on ARM processor. Speech technology is not 100% reliable but using an embedded microphone array increases speech in very noisy environments. Face Recognition is the process of matching the detected 'face' to one of many faces known to the file system. The system consists of three parts. First users pronounce a password through MIC which he enrolled and the MIC is present on ARM 9 board. Secondly, using an usb camera interface images is captured and the image is processed with help of Opencv and compared with existing database. And finally if the current image is match with any of existing image the system forwards user password to owner mobile through GSM module. The main aim of this project is to recognize the voice and face of the user at the door based on ARM processor. The algorithms were implemented in the system using C++. The system uses the Intel OpenCV library for image processing. However, when we use OpenCV library to detect a frontal face in an image using its Haar Cascade classifier face Detector, this will increase the human computer interaction by using real time face recognition. Phonon multimedia framework is used to display the image of the user. So that when user place face in front of camera it display recognize image of the user.

Automatic Craniofacial Anthropometry Landmarks Detection and Measurements for the Orbital Region

Facial landmarks detection is undoubtedly important in many applications in computer vision for example the face detection and recognition. In craniofacial anthropometry, consistent landmarks localization as per standard definition of the craniofacial anthropometry landmarks is very important in order to get accurate craniofacial anthropometry data. In this article we demonstrated an automatic detection of craniofacial anthropometry landmarks at the orbital region. 3D images of 100 respondents’ were photogaphed using Vectra-3D in controlled environment. Craniofacial measurements of 30 3D images were measured using VAM software. Two data sets of left and right eyes positive training data were created to train ‘en’ and ‘ex’ haar cascade classifiers. These classifiers were used to detect and locate the inner (en) and outer (ex) eye corners. We automatically measured the left and right eye fissures length (en-ex), the intercanthal (en-en) and the biocular (ex-ex) width. Statistical analysis was performed on the measurements taken by Vectra 3D and by our software with paired t-test and calculated the ICC indices. We observed quite amount of false positive detections. We removed the false positive and predicted the eye corners. Our classifiers able to detect and locate the ‘en’ and the ‘ex’ in 59 out of 60 test images. Our results show accurate detection of ‘ex’ and ‘en’ craniofacial landmarks as per standard definition. The paired t-test showed that all four (4) measurements are no significant difference with the p values on 95% confidence level are above 0.05. The ICC indices for the measurements were from 0.4 to 0.78. In conclusion, our trained enHaar and exHaar cascade classifiers were able to automatically detect the ‘en’ and ‘ex’ craniofacial anthropometry landmarks in controlled environment. The measurements were clinically no significant differences with the mean different were less than 1mm in both eye fissures and intercanthal except the biocular width (1.16mm). The consistency of the measurements between the two methods are good for the intercanthal width and moderate for the biocular width and for both eye fissure lengths.

Efficient Eye Blink Detection Method for disabled-helping domain

In this paper, we present a real time method based on some video and image processing algorithms for eye blink detection. The motivation of this research is the need of disabling who cannot control the calls with human mobile interaction directly without the need of hands. A Haar Cascade Classifier is applied for face and eye detection for getting eye and facial axis information. In addition, the same classifier is used based on Haar- like features to find out the relationship between the eyes and the facial axis for positioning the eyes. An efficient eye tracking method is proposed which uses the position of detected face. Finally, an eye blinking detection based on eyelids state (close or open) is used for controlling android mobile phones. The method is used with and without smoothing filter to show the improvement of detection accuracy. The application is used in real time for studying the effect of light and distance between the eyes and the mobile device in order to evaluate the accuracy detection and overall accuracy of the system. Test results show that our proposed method provides a 98% overall accuracy and 100% detection accuracy for a distance of 35 cm and an artificial light.

Embedded Vision System for Autonomous Face Recognizer Robot using Eigenface Detector-PCA Improved Face Detection for Largely Occluded Faces

The problem is to propose a method for detecting the face and recognizing the face for a robot. This is a challenging problem because faces are non-rigid and have a high degree of variability in size, shape, colour, and texture. In this report, an embedded vision system is built to locate, recognize and track the faces. Three individual sub-systems are present. The first one is face detection based on Haar-like classifier and ViolaJones object detector. The second one is face recognition system based on Eigenface detector using principal component analysis method. The third one is face tracking based on Haar Cascade Classifier. For fast and an efficient embedded vision system for an autonomous robot, the software used is OpenCV library. The system developed has improved performance and can also detect largely occluded faces.

Obscenity detection using haar-like features and Gentle Adaboost classifier.

Large exposure of skin area of an image is considered obscene. This only fact may lead to many false images having skin-like objects and may not detect those images which have partially exposed skin area but have exposed erotogenic human body parts. This paper presents a novel method for detecting nipples from pornographic image contents. Nipple is considered as an erotogenic organ to identify pornographic contents from images. In this research Gentle Adaboost (GAB) haar-cascade classifier and haar-like features used for ensuring detection accuracy. Skin filter prior to detection made the system more robust. The experiment showed that, considering accuracy, haar-cascade classifier performs well, but in order to satisfy detection time, train-cascade classifier is suitable. To validate the results, we used 1198 positive samples containing nipple objects and 1995 negative images. The detection rates for haar-cascade and train-cascade classifiers are 0.9875 and 0.8429, respectively. The detection time for haar-cascade is 0.162 seconds and is 0.127 seconds for train-cascade classifier.

Part-Based Pedestrian Detection and Feature-Based Tracking for Driver Assistance: Real-Time, Robust Algorithms, and Evaluation

Detecting pedestrians is still a challenging task for automotive vision systems due to the extreme variability of targets, lighting conditions, occlusion, and high-speed vehicle motion. Much research has been focused on this problem in the last ten years and detectors based on classifiers have gained a special place among the different approaches presented. This paper presents a state-of-the-art pedestrian detection system based on a two-stage classifier. Candidates are extracted with a Haar cascade classifier trained with the Daimler Detection Benchmark data set and then validated through a part-based histogram-of-oriented-gradient (HOG) classifier with the aim of lowering the number of false positives. The surviving candidates are then filtered with feature-based tracking to enhance the recognition robustness and improve the results' stability. The system has been implemented on a prototype vehicle and offers high performance in terms of several metrics, such as detection rate, false positives per hour, and frame rate. The novelty of this system relies on the combination of a HOG part-based approach, tracking based on a specific optimized feature, and porting on a real prototype.

Face detection evaluation: a new approach based on the golden ratio $${\Phi}$$

Face detection is a fundamental research area in computer vision field. Most of the face-related applications such as face recognition and face tracking assume that the face region is perfectly detected. To adopt a certain face detection algorithm in these applications, evaluation of its performance is needed. Unfortunately, it is difficult to evaluate the performance of face detection algorithms due to the lack of universal criteria in the literature. In this paper, we propose a new evaluation measure for face detection algorithms by exploiting a biological property called Golden Ratio of the perfect human face. The new evaluation measure is more realistic and accurate compared to the existing one. Using the proposed measure, five haar-cascade classifiers provided by Intel©OpenCV have been quantitatively evaluated on three common databases to show their robustness and weakness as these classifiers have never been compared among each other on same databases under a specific evaluation measure. A thoughtful comparison between the best haar-classifier and two other face detection algorithms is presented. Moreover, we introduce a new challenging dataset, where the subjects wear the headscarf. The new dataset is used as a testbed for evaluating the current state of face detection algorithms under the headscarf occlusion.

Webcam-Based 2D Eye Gaze Estimation System By Means of Binary Deformable Eyeball Templates

Eye gaze as a form of input was primarily developed for users who are unable to use usual interaction devices such as keyboard and the mouse; however, with the increasing accuracy in eye gaze detection with decreasing cost of development, it tends to be a practical interaction method for able-bodied users in soon future as well. This paper explores a low-cost, robust, rotation and illumination independent eye gaze system for gaze enhanced user interfaces. We introduce two brand-new algorithms for fast and sub-pixel precise pupil center detection and 2D Eye Gaze estimation by means of deformable template matching methodology. In this paper, we propose a new algorithm based on the deformable angular integral search algorithm based on minimum intensity value to localize eyeball (iris outer boundary) in gray scale eye region images. Basically, it finds the center of the pupil in order to use it in our second proposed algorithm which is about 2D eye gaze tracking. First, we detect the eye regions by means of Intel OpenCV AdaBoost Haar cascade classifiers and assign the approximate size of eyeball depending on the eye region size. Secondly, using DAISMI (Deformable Angular Integral Search by Minimum Intensity) algorithm, pupil center is detected. Then, by using the percentage of black pixels over eyeball circle area, we convert the image into binary (Black and white color) for being used in the next part: DTBGE (Deformable Template based 2D Gaze Estimation) algorithm. Finally, using DTBGE algorithm, initial pupil center coordinates are assigned and DTBGE creates new pupil center coordinates and estimates the final gaze directions and eyeball size. We have performed extensive experiments and achieved very encouraging results. Finally, we discuss the effectiveness of the proposed method through several experimental results.

The architecture and performance of the face and eyes detection system based on the Haar cascade classifiers

The precise face and eyes detection is essential in many human---machine interface systems. Therefore, it is necessary to develop a reliable and efficient object detection method. In this paper we present the architecture of a hierarchical face and eyes detection system using the Haar cascade classifiers (HCC) augmented with some simple knowledge-based rules. The influence of the training procedure on the performance of the particular HCCs has been investigated. Additionally, we compared the efficiency of other authors' face and eyes HCCs with the efficiency of those trained by us. By applying the proposed system to the set of 10,000 test images we were able to properly detect and precisely localize 94% of the eyes.