Face Recognition Using Image Processing for Visually Challenged

2020 ◽  
Author(s):  
Shivam Bansal
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Visually Challenged

An Improved LBP Blockwise Method for Face Recognition

2018 ◽  
Vol 7 (4) ◽  
pp. 45-55 ◽  
Author(s):  
Nikhil Kumar ◽  
Sunny Behal
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Human Brain ◽  
Digital Image Processing ◽  
Digital Image ◽  
Processing System ◽  
Automatic Processing ◽  
Image Databases ◽  
Similar Kind

Face recognition is considered as one of toughest and most crucial leading domains of digital image processing. The human brain also uses a similar kind of technique for face recognition. When scrutinizing a face, the human brain signifies the result. Aside from AN automatic processing system, this technique is very sophisticated, owing to the image variations on account of the picture varieties in as far as area, size, articulation, and stance. In this article, the authors have used the options of native binary pattern and uniform native binary pattern for face recognition. They compute a number of classifiers on publicly available benchmarked ORL image databases to validate the proposed approach. The results clearly show that the proposed LBP-piece shrewd strategy has outperformed the traditional LBP system.

scholarly journals A SMART IMAGE PROCESSING ALGORITHM FOR TEXT RECOGNITION, INFORMATION EXTRACTION AND VOCALIZATION FOR THE VISUALLY CHALLENGED

2019 ◽  
Vol 1 (01) ◽  
pp. 31-38 ◽  
Author(s):  
Samuel Manoharan
Keyword(s):  
Image Processing ◽  
Time Delay ◽  
Information Extraction ◽  
Conversion Process ◽  
Text Recognition ◽  
Post Processing ◽  
Text To Speech ◽  
Image Processing Algorithm ◽  
Scanned Images ◽  
Visually Challenged

This paper proposes a smart algorithm for image processing by means of recognition of text, extraction of information and vocalization for the visually challenged. The system uses LattePanda Alpha system on board that processes the scanned images. The image is categorized into its equivalent alphanumeric characters following pre-processing, segmentation, extraction of features and post-processing of the scanned or image based information. Further, a text to speech synthesizer is used for vocalization processed content. In converting handwritten scripts, the system offers an accuracy of 97% in conversion. This also depends on the legibility of the data. The time delay for the entire conversion process is also analysed and the efficiency of the system is estimated.

Fast Face Recognition on GPUs

2014 ◽  
Vol 905 ◽  
pp. 543-547
Author(s):  
Yi Lei ◽  
Xiao Ya Fan ◽  
Meng Zhang
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Face Recognition ◽  
Processing Speed ◽  
Principal Component ◽  
Great Accuracy ◽  
Analysis Algorithm ◽  
Training Images ◽  
Principal Component Analysis Algorithm

Face recognition is popular in the field of pattern recognition and image processing. However, traditional recognition technologies spend too long there are a lot of images to be recognized or trained for great accuracy in the recognition. Parallel computing is an effective way to improve the processing speed. With the improvement of GPU performance, its widely applied in computing-concentrated data operations. This paper presents a study of performance speedup achieved by applying GPU for face recognition based on PCA (Principal Component Analysis) algorithm. We successfully accelerated the testing phase by 6868-folds compared to a sequential C implementation when it has 100 test images and 2400 training images.

scholarly journals Deep Learning- Based Surveillance System using Face Recognition

2020 ◽  
Vol 32 ◽  
pp. 03011
Author(s):  
Divya Kapil ◽  
Aishwarya Kamtam ◽  
Akhil Kedare ◽  
Smita Bharne
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Surveillance System ◽  
Surveillance Systems ◽  
Learning Method ◽  
Security Systems ◽  
The Face

Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. The system learns changes while detecting faces automatically using deep learning and gain correct accuracy in face recognition. A deep learning method including Convolutional Neural Network (CNN) is having great significance in the area of image processing. This system can be applicable to monitor the activities for the housing society premises.

The Effect of Feature Displacement on Face Recognition

Perception ◽  
1984 ◽  
Vol 13 (5) ◽  
pp. 505-512 ◽  
Author(s):  
Nigel D Haig
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Visual Recognition ◽  
High Sensitivity ◽  
Human Beings ◽  
Monitor Screen ◽  
High Significance ◽  
Facial Images ◽  
Vertical Positioning ◽  
Very High

Human beings possess a remarkable ability to recognise familiar faces quickly and without apparent effort. In spite of this facility, the mechanisms of visual recognition remain tantalisingly obscure. An experiment is reported in which image processing equipment was used to displace slightly the features of a set of original facial images to form groups of modified images. Observers were then required to indicate whether they were being shown the “original” or a “modified” face, when shown one face at a time on a TV monitor screen. Memory reinforcement was provided by displaying the original face at another screen position, between presentations. The data show, inter alia, the very high significance of the vertical positioning of the mouth, followed by eyes, and then the nose, as well as high sensitivity to close-set eyes, coupled with marked insensitivity to wide-set eyes. Implications of the results for the use of recognition aids such as Identikit and Photofit are briefly discussed.

scholarly journals Face Recognition Based on Statistical Texture Features

2021 ◽  
Vol 7 (1) ◽  
pp. 10-15
Author(s):  
Lama Akram Ibrahim ◽  
Nasser Nasser ◽  
Majd Ali
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Face Recognition ◽  
Processing Time ◽  
Facial Recognition ◽  
Texture Features ◽  
Memory Consumption ◽  
Methods And Techniques ◽  
Recognition Systems ◽  
Occurrence Matrix

Facial recognition has attracted the attention of researchers and has been one of the most prominent topics in the fields of image processing and pattern recognition since 1990. This resulted in a very large number of recognition methods and techniques with the aim of increasing the accuracy and robustness of existing systems. Many techniques have been developed to address the challenges and reliable recognition systems have been reached but require considerable processing time, suffer from high memory consumption and are relatively complex. The focus of this paper is on extracting subset of descriptors (less correlated and less calculations) from the co-occurrence matrix with the goal of enhancing the performance of Haralick’s descriptors. Improvements are achieved by adding the image pre-processing and selecting the proper method according to the database problem and by extracting features from image local regions.

scholarly journals Attendance Management System using Google Facenet for Facial Recognition

2021 ◽  
Vol 9 (VI) ◽  
pp. 5029-5036
Author(s):  
Rakesh Duggempudi
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Open Source ◽  
Management System ◽  
Facial Recognition ◽  
Cost Effective ◽  
Source Image ◽  
Student Attendance ◽  
Manual Work ◽  
View Update

Attendance management system is a required tool for attaining attendance in any habitat where attendance is essential. Yet, many of the available techniques consume time, are invasive and it demands manual work from the users. This research is directed at building a less invasive, cost effective and more efficient automated student attendance management system using face recognition that leverages on OpenCV functions for facial recognition. The system provides a GUI for marking attendance. It provides an interface for updating attendance using facial recognition libraries of OpenCV. The system stores attendance in a database which is maintained by the administrator. The administrator can view, update, and change the attendance of the students. The students can view and update their attendance. The system is developed on Open-Source image processing library and the interface is developed using Python Tkinter module. The Tkinter module is an open-source module by which we can develop GUI screens hence, it is not software dependent nor vendor hardware. The OpenCV module used for image processing is interfaced using python.

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