scholarly journals Implementasi Algoritma Haar Cascade pada Aplikasi Pengenalan Wajah

2021 ◽  
Vol 3 (1) ◽  
pp. 33-38
Author(s):  
Febiannisa Utami ◽  
Suhendri Suhendri ◽  
Muhammad Abdul Mujib
Keyword(s):  
Face Detection ◽  
Detection System ◽  
Image Data ◽  
Image Process ◽  
Cascade Algorithm ◽  
Ip Camera ◽  
The Face ◽  
Face Detection And Recognition ◽  
Haar Cascade ◽  
Detection And Recognition

The large number of citizens in an organization makes the development of an attendance system or citizen detection in a place important in the running of work activities in the organization. Utilization of an IP Camera which is only used for regular monitoring without further detection of the needs of citizens in the organization made the development of personnel detection developed for monitoring the presence of personnel. With the development of a face detection system, it is hoped that the facial algorithm development system will be developed using an IP Camera. Face detection has been developed which has many and special features which aim to determine whether or not a face has been detected in an image. With image management that is developed in face detection, detection will be faster and more accurate because the color is processed into gray degrees so that there are fewer color pixels than those with colors. By using the Python programming language and an image detection library called OpenCV, less code will be designed. This study uses the Viola Jones method, which is a fast and accurate face detection method developed by Paul Viola and Michael Jones. In this study, the Viola Jones method uses the Haar Cascade algorithm which functions as a detection feature in the system and is combined with the internal image process and the AdaBoost Learning and Cascade Classifier so that the detected face object will easily classify whether the object is a face or not. In this case the Cascade Classfier used in this study is the face and eyes. The development of this algorithm is carried out for face detection and recognition. The detection is done by taking pictures with the process taken using a webcam. The system will take several pictures and then the image data will be stored in a folder called dataSet. After that, all data is trained so that it can be recognized by the system. With retrieval, detection and recognition limitations that can only be taken from a distance of less than three meters, face detection on the IP Camera can still read objects other than faces. With recognition and accuracy on the webcam camera, about 80,5% this system can be developed with the Haar Cascade algorithm and the Haar Cascade algorithm precisely to be applied to the development of faced detection and face recognition. By developing the Haar Cascade algorithm for face detection, problems and utilization of an organization's data can be more easily detected and used by IP cameras that can support the performance process of face detection and recognition

scholarly journals An Efficient Method for Multiple Face Detection & Recognition

2019 ◽  
Vol 9 (1S3) ◽  
pp. 474-478
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Efficient Method ◽  
Recognition Rate ◽  
The Future ◽  
Web Camera ◽  
The Face ◽  
Face Detection And Recognition ◽  
Future Work ◽  
Detection And Recognition

The problem of Face detection and recognition is becoming a challenge due to the wide variety of faces and the complexity of the noises and background of image. In this paper we have used C-sharp and Haar algorithm to detect the face. First in this paper the image is taken with a web-camera, storing it in the database and then once again when the person comes in the frame the name of the person is displayed. This paper is done in C-sharp which was a bit difficult for us to do and we have combined both the face detection and the recognition. The proposed method has good output and a good recognition rate. The limitation of the paper is that it does not display the name of the person above the face. In the future work will be carried on the above said topic. While developing the code some sample codes in python but those were basic programs. So this paper aims to find a solution for it and developed in C-sharp. In finding the XML file of haarcascade frontal face detection we found some problems and had to do a bit of research in finding it. The code for face detection and face recognition were found in different places and in this paper the codes for the both has been combined and found some difficulty. To overcome the basic programs we have written the code in C-sharp and the difficulty which we faced in combining the two codes have been solved. The solution has been successfully implemented and the code is fully running and the output has been successfully achieved.

OBJECT DETECTION USING VIOLA-JONES ALGORITHM

2018 ◽  
Vol 28 (4) ◽  
pp. 1349-1354
Author(s):  
Dušan Stevanović
Keyword(s):  
Face Detection ◽  
Three Dimensional ◽  
Human Head ◽  
Accurate Information ◽  
Feedback Information ◽  
Three Dimensional Objects ◽  
The Face ◽  
Face Detection And Recognition ◽  
False Recognitions ◽  
Detection And Recognition

In this paper it has been described and applied method for detecting face and face parts in images using the Viola-Jones algorithm. The work is based on Computer Vision Systems, artificial intelligence that deals with the recognition of two-dimensional or three-dimensional objects. When Cascade Object Detector script is trained, multimedia content is assigned for recognition. In this work the content will be in the form of an image, where the program will have the task of recognizing the objects in the images, separating the parts of the images in the head area, and on each discovered face, separately mark the area around the eyes, nose and mouth.Algorithm for detection and recognition is based on scanning and analyzing front part of human head. Common usage of face detection and recognition can be find in biometry, photography, on autofocus option which is implemented in professional photo cameras or on smiling detectors (Keller, 2007). Marketing is also popular field where face detection and recognition can be used. For example, web cameras that are implemented in TVs, can detect every face in near area. Calculating different type of algorithms and parameters, based on sex, age, ethnicity, system can play precisely segmented television commercials and campaigns. Example of that kind of systems is OptimEyes. (Strasburger, 2013)In other words, every algorithm that has as its main goal to detect and recognize face from image, should give as a feedback information, is there any face and if answer is positive, where is its location on image. In order to achieve acceptable performances, algorithm should minimize false recognitions. These are the cases when the algorithm ignores and does not recognize the real object from the image, and vice versa, when the wrong object is recognized as real. One of the algorithms that is frequently applied in this area of research is the Viola-Jones algorithm. This algorithm is functional in real time, meaning that besides detection, it is also possible to adjust the ability to monitor faces from video material.In this paper, the problem that will be analyzed is facial image detection. Man can do this task in a very simple way, but to do the same with a computer, it is necessary to have a range of precise and accurate information, formulas, methods and techniques. In order to maximize the precision of recognizing the face of the image using the Viola-Jones algorithm, it is desirable that the objects in the images are completely face-to-face with the image-taking device, which will be shown through experiments.

scholarly journals Sparse Neural Network for horse face detection in a Smart Riding Club Biometric System

2021 ◽  
Author(s):  
Islem Jarraya ◽  
Wael Ouarda ◽  
Fatma BenSaid ◽  
Adel Alimi
Keyword(s):  
Neural Network ◽  
Face Detection ◽  
Detection System ◽  
Recognition Rate ◽  
Biometric System ◽  
Training Time ◽  
Face Detection And Recognition ◽  
Global And Local ◽  
Sparse Features ◽  
Detection And Recognition

Horses and breeders need to be safe on the farm and the riding club. On account of the great value of the horse, the breeder needs to protect it from theft and disease. In this context, it is important to detect and to recognize the identity of each horse for security reasons. In fact, this paper proposes a Smart Riding Club Biometric System (SRCBS) consisting in automatically detecting and recognizing horses as well as humans. The proposed system is based on the facial biometrics for a horse and the gait biometrics for a human due to their simplicity and intuitiveness in an uncontrolled environment. The present work focuses mainly on horse face detection and recognition. Animal face detection is still extremely difficult given the fact that face textures and shapes are grossly diverse. In addition, recent detectors require a huge dataset for training and represent a huge number of parameters and layers, leading to so much training time. For resolving these problems and also for a useful detection system, this paper proposes a Sparse Neural Network (SNN) based on sparse features for horse face detection.<br>Different global and local features were performed to identify horses and humans for the recognition process. Due to the unavailability of horse databases, this paper presents a new benchmark for horse detection and recognition in order to evaluate our proposed system. This system achieved an average precision equal to 90% for horse face detection and a recognition rate equal to 99.89% for horse face identification.

scholarly journals Face with Mask Detection and Recognition for Smart Attendance System

2021 ◽  
Vol 9 (VII) ◽  
pp. 1586-1591
Author(s):  
Shweta Panjabrao Dhawale
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Face Mask ◽  
Learning Framework ◽  
The Face ◽  
Face Detection And Recognition ◽  
Python Programming ◽  
Detection And Recognition

In this paper we will see the face mask detection and recognition for smart attendance system. In current pandemic situation our proposed system is very useful. We have used here face algorithm technique, python programming and to capture the images open cv is used., open cv2 now comes with a very new face recognizer class for the face recognition and popular computer vision liberaay started by intel in 1999. Open cv released under BSD licence that’s why used in the academic projects. We have used the concept of deep learning framework for the detection of faces. our aim is to present the study of previous attempts at face detection and recognition for smart attendance system by using deep learning .these is rapidly growing technology with its application in various aspects.

scholarly journals Face Detection and Recognition Based on Visual Attention Mechanism Guidance Model in Unrestricted Posture

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Zhenguo Yuan
Keyword(s):  
Visual Attention ◽  
Face Detection ◽  
Attention Mechanism ◽  
Additional Parameter ◽  
Detection Problem ◽  
The Face ◽  
Face Detection And Recognition ◽  
Visual Attention Mechanism ◽  
Guidance Model ◽  
Detection And Recognition

Performance of face detection and recognition is affected and damaged because occlusion often leads to missed detection. To reduce the recognition accuracy caused by facial occlusion and enhance the accuracy of face detection, a visual attention mechanism guidance model is proposed in this paper, which uses the visual attention mechanism to guide the model highlight the visible area of the occluded face; the face detection problem is simplified into the high-level semantic feature detection problem through the improved analytical network, and the location and scale of the face are predicted by the activation map to avoid additional parameter settings. A large number of simulation experiment results show that our proposed method is superior to other comparison algorithms for the accuracy of occlusion face detection and recognition on the face database. In addition, our proposed method achieves a better balance between detection accuracy and speed, which can be used in the field of security surveillance.

scholarly journals REAL TIME FACE DETECTION AND RECOGNITION SYSTEM USING HAAR CASCADE CLASSIFIER AND NEURAL NETWORKS

2021 ◽  
Vol 9 (1) ◽  
pp. 224-231
Author(s):  
Anirban Chakraborty, Shilpa Sharma
Keyword(s):  
Face Detection ◽  
Facial Recognition ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Smart Device ◽  
Cascade Classifier ◽  
Face Detection And Recognition ◽  
Haar Cascade ◽  
The Times ◽  
Detection And Recognition

Home protection and privacy have become one of the most critical aspects in today's world. As technology progresses at an exponential pace, the times are not far ahead for each house to be fitted with sophisticated security systems to deal with regular burglary and theft. But as one side of the tech progresses, so do its detrimental counterparts. DES encryption can be an indicator of how easily an encrypted piece of information can be deciphered. Not long after its release, DES encryption was referred to as 'unsafe' and with today's modern application, anything like DES might be an open invitation to hack. With many developments in the field, the technology has, in many respects, surpassed the use of biometrics (finger prints). Face recognition, nowadays, is present in almost every smart device that has some piece of information stored that holds importance to its users. With facial recognition gaining popularity, many tech companies have come with their own patent to make a technology related to Facial Recognition on the market. This paper suggests a somewhat related concept as to how home protection can be improved by using a face detection and recognition algorithm (Haar Cascade Classifier).

Comparison of Face Detection and Recognition Algorithms in Real-Time Video

2020 ◽  
Author(s):  
Alejandra Sarahi Sanchez-Moreno ◽  
Hector Manuel Perez-Meana ◽  
Jesus Olivares-Mercado ◽  
Gabriel Sanchez-Perez ◽  
Karina Toscano-Medina
Keyword(s):  
Real Time ◽  
Face Detection ◽  
High Performance ◽  
Facial Recognition ◽  
Rapid Development ◽  
Real Time Systems ◽  
The Face ◽  
Face Detection And Recognition ◽  
Time Systems ◽  
Detection And Recognition

Facial recognition systems has captivated research attention in recent years. Facial recognition technology is often required in real-time systems. With the rapid development, diverse algorithms of machine learning for detection and facial recognition have been proposed to address the challenges existing. In the present paper we proposed a system for facial detection and recognition under unconstrained conditions in video sequences. We analyze learning based and hand-crafted feature extraction approaches that have demonstrated high performance in task of facial recognition. In the proposed system, we compare different traditional algorithms with the avant-garde algorithms of facial recognition based on approaches discussed. The experiments on unconstrained datasets to study the face detection and face recognition show that learning based algorithms achieves a remarkable performance to face the challenges in real-time systems.

scholarly journals Sparse Neural Network for horse face detection in a Smart Riding Club Biometric System

2021 ◽  
Author(s):  
Islem Jarraya ◽  
Wael Ouarda ◽  
Fatma BenSaid ◽  
Adel Alimi
Keyword(s):  
Neural Network ◽  
Face Detection ◽  
Detection System ◽  
Recognition Rate ◽  
Biometric System ◽  
Training Time ◽  
Face Detection And Recognition ◽  
Global And Local ◽  
Sparse Features ◽  
Detection And Recognition

Horses and breeders need to be safe on the farm and the riding club. On account of the great value of the horse, the breeder needs to protect it from theft and disease. In this context, it is important to detect and to recognize the identity of each horse for security reasons. In fact, this paper proposes a Smart Riding Club Biometric System (SRCBS) consisting in automatically detecting and recognizing horses as well as humans. The proposed system is based on the facial biometrics for a horse and the gait biometrics for a human due to their simplicity and intuitiveness in an uncontrolled environment. The present work focuses mainly on horse face detection and recognition. Animal face detection is still extremely difficult given the fact that face textures and shapes are grossly diverse. In addition, recent detectors require a huge dataset for training and represent a huge number of parameters and layers, leading to so much training time. For resolving these problems and also for a useful detection system, this paper proposes a Sparse Neural Network (SNN) based on sparse features for horse face detection.<br>Different global and local features were performed to identify horses and humans for the recognition process. Due to the unavailability of horse databases, this paper presents a new benchmark for horse detection and recognition in order to evaluate our proposed system. This system achieved an average precision equal to 90% for horse face detection and a recognition rate equal to 99.89% for horse face identification.

scholarly journals PEMANFAATAN RASPBERRY PI SEBAGAI PROCESSOR PADA PENDETEKSIAN DAN PENGENALAN POLA WAJAH

2016 ◽  
Vol 7 (3) ◽  
Author(s):  
Fadli Sirait ◽  
Yoserizal Yoserizal
Keyword(s):  
Discriminant Analysis ◽  
Embedded System ◽  
Face Detection ◽  
Principal Component ◽  
Raspberry Pi ◽  
Cascade Classifier ◽  
Linear Discriminant ◽  
Face Detection And Recognition ◽  
Haar Cascade ◽  
Detection And Recognition

Teknologi biometrik adalah teknologi untuk mengindetifikasi mahluk hidup. Tujuan perancangan  adalah  untuk  membangun  sistem  pendeteksi  wajah  dari  objek  citra  yang  didapat dari  gambar  frame  video  melalui  kamera.  Kemudian  dilakukan  pendeteksi  pola  wajah  yang dikenali  dan  mencari  kemiripan  terhadap  database  model  wajah  menggunakan  Raspberri  Pi berbasis  penggunaan  perangkat  lunak  Free  dan  Opensource.  Perancangan  ini  menggunakan metode  pengenalan  objek  citra  wajah  dengan  Haar  Cascade  Classifier  yang  diimplentasikan pada libarary OpenCV, sedangkan metode pengenalan pola wajah dengan menggunakan analisa PCA (Principal Component Analysist) dan LDA (Linear Discriminant Analysis) menggunakan pemograman  prerangkat  lunak  yang  dibuat  berbasis  Python.  Perangkat  lunak  yang dikembangkan  juga  dijalankan  pada  sistem  operasi  berbasis  Linux  Raspbian  (Jessie  dan Wheezy)  yang  diinstal  di  Raspberry  Pi.  Proses  input  citra  menggunakan  USB  kamera  yang dipasang  pada  Raspberry  Pi  2  Model  B  yang  dilengkapi  dengan  LCD  3,5  inchi.  Berdasarkan data pengujian terhadap 127 input didapat tingkat akurasi untuk pendeteksian satu objek wajah 84-97% sementara performa penggunaan CPU pada Raspberry Pi 41.87-46.25%.Kata kunci: face detection and recognition, Raspberry pi, pengolahan citra, embedded system.

scholarly journals Face Detection with Machine Learning and Open CV Classifier

2020 ◽  
pp. 100-106
Keyword(s):  
Face Detection ◽  
Detection System ◽  
Recording System ◽  
Cascade Classifier ◽  
Image Evaluation ◽  
Facial Hair ◽  
Lighting Conditions ◽  
The Face ◽  
Haar Cascade ◽  
Human Faces

In the last few years, face recognitions owned considerable consideration and liked together of the foremost used functions within the area of image evaluation and recognition. Face detection reflects on consideration of an incredible section of face attention operations. The technique of face detection in pixels is elaborate with many features’ variabilities provided throughout human faces. Faces include pose, expression, smile, role and orientation, pores and complexion, the presence of glasses or facial hair, variations in digicam gain, lighting conditions, and photo resolutions. Haar Cascade classifier is of outstanding assist when performing this undertaking smoothly. Face detection goes to possess a dramatic impression on the face detection field, as a result, familiarizing yourself with its functions like attendance recording system with the help of camera, Mask detection system. In this paper, we proposed a face detection system for the utilization of computer learning, especially OpenCV. The mandatory step required is face detection which we did with the usage of a broadly used step referred to as the haarcascade_frontalface_default classifier, python and its module.

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