Local Binary Pattern Histogram Method Research Articles

A Real Time Facial Recognition and Tracking System for Personnel Presence

Abstract: Our project aims to revolutionize the conventional attendance system by implementing a facial recognition-based solution. The existing manual methods are prone to human error and require significant maintenance. By leveraging facial recognition technology, our system will offer improved precision and efficiency, reducing the need for manual work. The system will maintain a database of students' images, matching them during class to mark attendance accurately. Utilizing machine learning techniques, specially the Haar-Cascade classifier and local binary pattern histogram method for face detection and recognition, respectively, our system will ensure reliable attendance tracking. The attendance data will be stored in a MySQL database and Microsoft Excel file, providing a streamlined and secure alternative to the traditional attendance process.

Robot Vision Pattern Recognition of the Eye and Nose Using the Local Binary Pattern Histogram Method

The local binary pattern histogram (LBPH) algorithm is a computer technique that can detect a person's face based on information stored in a database (trained model). In this research, the LBPH approach is applied for face recognition combined with the embedded platform on the actuator system. This application will be incorporated into the robot's control and processing center, which consists of a Raspberry Pi and Arduino board. The robot will be equipped with a program that can identify and recognize a human's face based on information from the person's eyes and nose. Based on the results of facial feature identification testing, the eyes were recognized 131 times (87.33%), and the nose 133 times (88.67%) out of 150 image data samples. From the test results, an accuracy rate of 88%, the partition rate of 95.23%, the recall of 30%, the specificity of 99%, and the F1-Score of 57.5% were obtained.

SAFE: Security Door Lock System Using Haar-Cascade and LBPH Method

Door lock provides numerous benefits and has become indispensable in daily life as it acts as a security guard to prevent burglars and protect home belonging safely. The unlock methods of existing door lock system widely use keys and thumbprint involve touching the object may cause the spread of COVID-19. In this paper, a computer vision based security door lock system using Raspberry Pi (called SAFE) is proposed. Haar-Cascade classifier is employed as face detection classifier, while Local Binary Pattern Histogram (LBPH) is proposed as face recognition classifier. Recognition result is processed based on the usage of user to provide insights of SAFE. The accuracy of SAFE using pre-trained LBPH classifier achieves average of 86% based on the data obtained. The recognition speed outperforms existing work using principal component analysis and eigenfaces.

Detection & recognition of veiled and unveiled human face on the basis of eyes using transfer learning.

Face detection and recognition are the most substantial research areas in computer vision and transfer learning due to the inspiring nature of faces as an object. In this paper, we show that we can obtain promising results on the standard face databanks when the features are extracted merely from the eye. The contributions of this work are divided into three parts, specifically face detection, eyes detection and recognition for individual identification. The key features for face recognition, used in this study are the eyes, nostrils, and mouth. The key features for eyes recognition are center of left eye, center of right eye, midpoint of eyes and extraction of eyebrows. Extracted Local Binary Pattern Histogram (LBPH) method is used to extract the facial features of face images whose computational complexity is very low and these features contain simple pixel values. Furthermore, neighborhood pixels are calculated to extract effective facial feature to realize eyes recognition and person verification. This study is able to identify an individual on the basis of even a single eye. The algorithm finds the brighter eye from the face and then, on the basis of that eye, the person is identified and the name of person is provided. The experimental results of this study show that faces are recognized accurately and LBPH method has achieved 98.2% accuracy.

Facial Recognition Technique with Combination Method of Local Binary Pattern Histogram and Image Euclidean Distance

This study aims to determine the level of precision, recall, and accuracy of performance of local binary pattern histogram and image euclidean distance for face image recognition. This research uses local binary pattern histogram method for segmenting face image and euclidean distance image for face image recognition. The proposed method started with the process of converting pixel value into a binary value, which then the value is used as a decimal value and the histogram is calculated, for then saved in the database. The trained facial image histogram will be compared with the histogram of the test data using the euclidean distance image formula. The result of this research shows realtime test result get 0.699 recall value and 0.7 precision value.

Sistem Pengenalan Wajah dengan Algoritma Haar Cascade dan Local Binary Pattern Histogram

Recently, the applications of face recognition are increasing significantly. Some methods have already been tried, but the results have not optimal yet. This paper tries to overcome this problem, using haar cascade as face detection algorithm, whereas face recognition uses local binary pattern histogram method. This system uses a webcam as a camera and programming exploit OpenCV library. This system enables to differentiate the face of the human with others objects with the best range from the camera to the object is 50 cm until 150 cm. In addition, this system is capable to recognize faces from the 6 subjects of faces listed in the database, alone and in a group as well in one frame.

UAV-Based IoT Platform: A Crowd Surveillance Use Case

Unmanned aerial vehicles are gaining a lot of popularity among an ever growing community of amateurs as well as service providers. Emerging technologies, such as LTE 4G/5G networks and mobile edge computing, will widen the use case scenarios of UAVs. In this article, we discuss the potential of UAVs, equipped with IoT devices, in delivering IoT services from great heights. A high-level view of a UAV-based integrative IoT platform for the delivery of IoT services from large height, along with the overall system orchestrator, is presented in this article. As an envisioned use case of the platform, the article demonstrates how UAVs can be used for crowd surveillance based on face recognition. To evaluate the use case, we study the offloading of video data processing to a MEC node compared to the local processing of video data onboard UAVs. For this, we developed a testbed consisting of a local processing node and one MEC node. To perform face recognition, the Local Binary Pattern Histogram method from the Open Source Computer Vision is used. The obtained results demonstrate the efficiency of the MEC-based offloading approach in saving the scarce energy of UAVs, reducing the processing time of recognition, and promptly detecting suspicious persons.