Modified Local Binary Pattern Algorithm for Feature Dimensionality Reduction.

2019 ◽  
Vol 12 ◽  
Author(s):  
Manish Kumar ◽  
Rahul Gupta ◽  
Kota Solomon Raju ◽  
Dinesh Kumar ◽  
Dinesh Kumar
Keyword(s):  
Face Recognition ◽  
Dimensionality Reduction ◽  
Local Binary Pattern ◽  
Similarity Index ◽  
Recognition Algorithm ◽  
Query Image ◽  
Modified Method ◽  
Face Detection And Recognition ◽  
Feature Dimensionality Reduction ◽  
Maximum Similarity

A face recognition algorithm with feature dimensionality reduction is proposed. The proposed algorithm is based on a variant of Local Binary Pattern (LBP) for face detection and recognition. The features of each block of face image is extracted and then global feature of face is constructed from super histogram. For recognition, traditional methods are used. The query image is compared with the dataset (ORL Dataset, LFW Dataset and Yale Dataset) in similarity index and the minimum distance. The maximum similarity is used to define as the class of query image. The reduction in number of features is achieved by modifying the traditional LBP process. The proposed modified method is observed as more fast and efficient for face recognition as compared to the existing algorithms.

scholarly journals Intruder detection and recognition using different image processing techniques for a proactive surveillance

2021 ◽  
Vol 24 (2) ◽  
pp. 843
Author(s):  
Nelson C. Rodelas ◽  
Melvin A. Ballera
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Face Detection ◽  
Research Problem ◽  
Recognition Algorithm ◽  
Evaluation Framework ◽  
Image Processing Techniques ◽  
Face Detection And Recognition ◽  
Processing Techniques ◽  
Detection And Recognition

To innovate a proactive surveillance camera, there is a need for efficient face detection and recognition algorithm. The researchers used one of the ViolaJones algorithm and used different image processing techniques to recognize intruders or not. The goal of the research is to recognize the fastest way on how the homeowners will be informed if an intruder or burglar enters their home using a proactive surveillance device. This device was programmed based on the different recognition algorithms and a criteria evaluation framework that could recognize intruders and burglars and the design used was developmental research to satisfy the research problem. The researchers used the Viola-Jones algorithm for face detection and five algorithms for face recognition. The criteria evaluation was used to identify the best face recognition algorithm and was tested in a real-world situation and captured a series of images camera and processed by proactive face detection and recognition. The result shows that the system can detect and recognize intruders and proactively send a notification to the homeowners via mobile application. It is concluded that the system can recognize the intruders and proactively notify the household members using the mobile applications and activate the alarm system of the house.

scholarly journals Human Face Recognition using LBPH

2020 ◽  
Vol 8 (6) ◽  
pp. 3208-3212
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Face Detection ◽  
Local Binary Pattern ◽  
Human Face Recognition ◽  
Smart Systems ◽  
Finger Print ◽  
The Face ◽  
Face Detection And Recognition ◽  
Frame Work

During the beginning of seventieth centuries, human facial recognition has become one among the researched areas in the area of finger print scanning and computer vision. Identifying a person with an image has been popularized through the mass media. The recent technologies are totally focusing on developing the smart systems that will recognize the faces for biometric purposes. In this context automatic face recognition is applied for security purposes to find the criminal, attendance system, scientific laboratories etc. This research paper presents the frame work for real time face detection. However, it is less robust to finger print or retina scanning. This paper describes about the face detection and recognition. These technologies are available in the Open-Computer-Vision (OpenCV) library and methodology to implement them using Python in image processing and machine learning. For face detection, Haar-Cascades algorithms were used and for face recognition the algorithm like Eigen faces, and Local binary pattern histograms were used.

scholarly journals Robust face recognition based on multi-task convolutional neural network

2021 ◽  
Vol 18 (5) ◽  
pp. 6638-6651
Author(s):  
Huilin Ge ◽  
◽  
Yuewei Dai ◽  
Zhiyu Zhu ◽  
Biao Wang
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Face Detection ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Recognition Algorithm ◽  
Face Images ◽  
Robust Face Recognition

<abstract> <sec><title>Purpose</title><p>Due to the lack of prior knowledge of face images, large illumination changes, and complex backgrounds, the accuracy of face recognition is low. To address this issue, we propose a face detection and recognition algorithm based on multi-task convolutional neural network (MTCNN).</p> </sec> <sec><title>Methods</title><p>In our paper, MTCNN mainly uses three cascaded networks, and adopts the idea of candidate box plus classifier to perform fast and efficient face recognition. The model is trained on a database of 50 faces we have collected, and Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and receiver operating characteristic (ROC) curve are used to analyse MTCNN, Region-CNN (R-CNN) and Faster R-CNN.</p> </sec> <sec><title>Results</title><p>The average PSNR of this technique is 1.24 dB higher than that of R-CNN and 0.94 dB higher than that of Faster R-CNN. The average SSIM value of MTCNN is 10.3% higher than R-CNN and 8.7% higher than Faster R-CNN. The Area Under Curve (AUC) of MTCNN is 97.56%, the AUC of R-CNN is 91.24%, and the AUC of Faster R-CNN is 92.01%. MTCNN has the best comprehensive performance in face recognition. For the face images with defective features, MTCNN still has the best effect.</p> </sec> <sec><title>Conclusions</title><p>This algorithm can effectively improve face recognition to a certain extent. The accuracy rate and the reduction of the false detection rate of face detection can not only be better used in key places, ensure the safety of property and security of the people, improve safety, but also better reduce the waste of human resources and improve efficiency.</p> </sec> </abstract>

Prototype of Student Attendance Application Based on Face Recognition Using Eigenface Algorithm

2019 ◽  
Vol 3 (1) ◽  
pp. 23
Author(s):  
Tio Eko Prabowo ◽  
Rudy Hartanto ◽  
Sunu Wibirama
Keyword(s):  
Face Recognition ◽  
Light Intensity ◽  
Recognition Accuracy ◽  
Recognition Algorithm ◽  
Test Results ◽  
Student Attendance ◽  
Environmental Light ◽  
Student Manual ◽  
Face Detection And Recognition ◽  
Axis Rotation

Prototype of face recognition based attendance application that has been developed to overcome weaknesses in DTETI UGM student manual attendance system has several weaknesses. These weaknesses are a decrease in facial recognition accuracy when operating under conditions of varying environmental light intensity and in condition of face rotating towards z axis rotation centre. In addition, application prototype also does not yet have a database to store attendance results. In this paper, a new application prototype has been developed using Eigenface face detection and recognition algorithm and Haar-based Cascade Classifier. Meanwhile, to overcome prototype performance weaknesses of the previously developed application, a pre-processing method was proposed in another study was added. Processes in the method were geometry transformation, histogram levelling separately, image smoothing using bilateral filtering, and elliptical masking. The test results showed that in the category of various environmental light intensity conditions, face recognition accuracy from developed application prototypes was 16.71% better than previous application prototypes. Meanwhile, in category of face slope conditions at z axis rotation centre, face recognition accuracy from developed application prototype was 38.47% better. Attendance database system was also successfully implemented and running without error.

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