Semi-Automatic Ground Truth Annotation for Benchmarking of Face Detection in Video

2011 ◽  
pp. 187-207 ◽  
Author(s):  
Dzmitry Tsishkou ◽  
Liming Chen ◽  
Eugeny Bovbel
Keyword(s):  
Image Processing ◽  
Face Detection ◽  
Ground Truth ◽  
Current Approach ◽  
Video Sequences ◽  
Manual Annotation ◽  
Ground Truth Data ◽  
Sample Test ◽  
The Face ◽  
Facial Images

This work presents a method of semi-automatic ground truth annotation for benchmarking of face detection in video. We aim to illustrate the solution to the issue where an image processing and pattern recognition expert is able to label and annotate facial patterns in video sequences at the rate of 7500 frames per hour. We extend these ideas to the semi-automatic face annotation methodology, where all object patterns are categorized into 4 classes in order to increase flexibility of evaluation results analysis. We present a strict guide how to speedup manual annotation process by 30 times and illustrate it with the sample test video sequences that consists of more than 100000 frames, 950 individuals and 75000 facial images. Experimental evaluation of the face detection using the ground truth data, that was semi-automatically labeled, demonstrates effectiveness of current approach both for learning and test stages.

scholarly journals Face recognition attendance system using Local Binary Pattern (LBP)

2019 ◽  
Vol 8 (1) ◽  
pp. 239-245 ◽  
Author(s):  
Shamsul J. Elias ◽  
Shahirah Mohamed Hatim ◽  
Nur Anisah Hassan ◽  
Lily Marlia Abd Latif ◽  
R. Badlishah Ahmad ◽  
...  
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Face Detection ◽  
Local Binary Pattern ◽  
Recognition System ◽  
Automated System ◽  
Student Attendance ◽  
Recognition Method ◽  
Face Recognition System ◽  
The Face

Attendance is important for university students. However, generic way of taking attendance in universities may include various problems. Hence, a face recognition system for attendance taking is one way to combat the problem. This paper will present an automated system that will automatically saves student’s attendance into the database using face recognition method. The paper will elaborate on student attendance system, image processing, face detection and face recognition. The face detection part will be done by using viola-jones algorithm method while the face recognition part will be carried on by using local binary pattern (LBP) method. The system will ensure that the attendance taking process will be faster and more accurate.

scholarly journals Vehicle Vacant Seat Identification and Mask Detection using Image processing

2021 ◽  
Vol 9 (VII) ◽  
pp. 118-121
Author(s):  
Mr. Shubham Ingole
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Face Detection ◽  
Detection Technique ◽  
Processing Technology ◽  
Raspberry Pi ◽  
Human Being ◽  
People Tracking ◽  
Software Application ◽  
The Face

This article describes the technique of real-time face detection, mask detection, and vacant seat available in the vehicle. There are so many technologies for finding seat availability in the vehicle. But image processing technology is very popular today. Face detection is part of image processing. It is used to find the face of a human being in a certain area. Face detection is used in many applications, such as facial recognition, people tracking or photography. In this paper, the face detection technique is used to detect the vacant seat availability in the vehicle and also to detect whether the passenger wear the mask on his face or not. The webcam is installed in the vehicle and connected with the Raspberry Pi 3 model B. When the vehicle leaves the station, the webcam will capture images of the passengers in the seating area. The webcam will be mounted on the vehicle. The images will be adjusted and enhanced to reduce noise made by the software application. The system obtains the maximum number of passengers in the vehicle that processes the images and then calculates the availability of seats in the vehicle. In covid-19 situation mask detection is necessary. so this system also used to detect the mask on face.

scholarly journals Design and Implementation of Facial Recognition System for Visually Impaired using Image Processing

2019 ◽  
Vol 8 (4) ◽  
pp. 4803-4807
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Face Detection ◽  
Visually Impaired ◽  
Recognition System ◽  
Raspberry Pi ◽  
Design And Implementation ◽  
The Face ◽  
Visually Impaired Students ◽  
Facial Recognition System

One of the most difficult tasks faced by the visually impaired students is identification of people. The rise in the field of image processing and the development of algorithms such as the face detection algorithm, face recognition algorithm gives motivation to develop devices that can assist the visually impaired. In this research, we represent the design and implementation of a facial recognition system for the visually impaired by using image processing. The device developed consists of a programmed raspberry pi hardware. The data is fed into the device in the form of images. The images are preprocessed and then the input image captured is processed inside the raspberry pi module using KNN algorithm, The face is recognized and the name is fed into text to speech conversion module. The visually impaired student will easily recognize the person before him using the device. Experiment results show high face detection accuracy and promising face recognition accuracy in suitable conditions. The device is built in such a way to improve cognition, interaction and communication of visually impaired students in schools and colleges. This system eliminates the need of a bulk computer since it employs a handy device with high processing power and reduced costs.

scholarly journals An Accurate Facial Component Detection Using Gabor Filter

2017 ◽  
Vol 6 (3) ◽  
pp. 287-294
Author(s):  
K. Sudhakar ◽  
P. Nithyanandam
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Face Detection ◽  
Gabor Filter ◽  
Personal Identification ◽  
Main Role ◽  
Accurate Detection ◽  
The Face ◽  
Critical Task ◽  
Facial Component

Face detection is a critical task to be resolved in a variety of applications. Since faces include various expressions it becomes a difficult task to detect the exact output. Face detection not only play a main role in personal identification but also in various fields which includes but not limited to image processing, pattern recognition, graphics and other application areas. The proposed system performs the face detection and facial components using Gabor filter. The results show accurate detection of facial components

Attendance Recording System using Face Recognition

2021 ◽  
Vol 9 (VII) ◽  
pp. 432-434
Author(s):  
Chandan R
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Data Base ◽  
Face Detection ◽  
Recording System ◽  
Reference Image ◽  
Web Camera ◽  
The Face ◽  
System Time ◽  
The Web

Image processing automated attendance system is the system in which easiest way to record the attendance for organization .This system is based on the face detection and face recognition algorithms. For this we make use of “Image Processing” using “MATLAB”. The concept of this paper is to provide real time attendance of students in a class to the faculty’s data base. Automatically detects the student using the web camera and only detect the facial part of that particular image and the image undergoes the various techniques and will compare with reference image, Later the attendance of the student is updated .Thus with the help of this system time will be saved and it is so convenient to record the attendance at any time throughout the day.

scholarly journals Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection

Aerospace ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 30
Author(s):  
Jonas Aust ◽  
Sam Shankland ◽  
Dirk Pons ◽  
Ramakrishnan Mukundan ◽  
Antonija Mitrovic
Keyword(s):  
Image Processing ◽  
Defect Detection ◽  
False Negative ◽  
Ground Truth ◽  
Small Sample ◽  
Small Data ◽  
Data Sets ◽  
Combined System ◽  
Aviation Maintenance ◽  
Sample Test

Background—In the field of aviation, maintenance and inspections of engines are vitally important in ensuring the safe functionality of fault-free aircrafts. There is value in exploring automated defect detection systems that can assist in this process. Existing effort has mostly been directed at artificial intelligence, specifically neural networks. However, that approach is critically dependent on large datasets, which can be problematic to obtain. For more specialised cases where data are sparse, the image processing techniques have potential, but this is poorly represented in the literature. Aim—This research sought to develop methods (a) to automatically detect defects on the edges of engine blades (nicks, dents and tears) and (b) to support the decision-making of the inspector when providing a recommended maintenance action based on the engine manual. Findings—For a small sample test size of 60 blades, the combined system was able to detect and locate the defects with an accuracy of 83%. It quantified morphological features of defect size and location. False positive and false negative rates were 46% and 17% respectively based on ground truth. Originality—The work shows that image-processing approaches have potential value as a method for detecting defects in small data sets. The work also identifies which viewing perspectives are more favourable for automated detection, namely, those that are perpendicular to the blade surface.

scholarly journals Attendance Maintaining and Monitoring using Face Recognition

2020 ◽  
Vol 9 (4) ◽  
pp. 1823-1828
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Face Detection ◽  
Input Image ◽  
Vital Role ◽  
Work Place ◽  
Final Phase ◽  
The Face ◽  
Recognition Technique ◽  
Fingerprint Detection

Technology has been playing a vital role in this world, where the work and the work place become digitalized. The paper reviews on monitoring the attendance using image processing, which involves face detection, labeling the detected face, training a classifier based on labeled dataset, and face recognition. Former methods on monitoring the attendance includes signing the attendance registry, fingerprint detection and barcode scanning where delinquency may occur. To prevail over and to take the technology to subsequent level image processing has been incorporated. Proposed system employs, capturing of the face in various dimensions, labeling of the captured images that is stored in the database for training and testing phase. Using the gathered data the machine is trained to recognize the face to provide access to the employees or students in the organization. The final phase is to take the attendance and maintain the record on attending hours using face recognition technique in which the input image of the employees or students is given.

scholarly journals ERICA: Emulated Retinal Image CApture - A tool for testing, training and validating retinal image processing methods

2021 ◽  
Author(s):  
Laura K Young ◽  
Hannah E Smithson
Keyword(s):  
Image Processing ◽  
Adaptive Optics ◽  
Retinal Image ◽  
Imaging System ◽  
Ground Truth ◽  
Learning Approaches ◽  
Image Capture ◽  
Ground Truth Data ◽  
Synthetic Images

ABSTRACTHigh resolution retinal imaging systems, such as adaptive optics scanning laser ophthalmoscopes (AOSLO), are increasingly being used for clinical and fundamental studies in neuroscience. These systems offer unprecedented spatial and temporal resolution of retinal structures in vivo. However, a major challenge is the development of robust and automated methods for processing and analysing these images. We present ERICA (Emulated Retinal Image CApture), a simulation tool that generates realistic synthetic images of the human cone mosaic, mimicking images that would be captured by an AOSLO, with specified image quality and with corresponding ground truth data. The simulation includes a self-organising mosaic of photoreceptors, the eye movements an observer might make during image capture, and data capture through a real system incorporating diffraction, residual optical aberrations and noise. The retinal photoreceptor mosaics generated by ERICA have a similar packing geometry to human retina, as determined by expert labelling of AOSLO images of real eyes. In the current implementation ERICA outputs convincingly realistic en face images of the cone photoreceptor mosaic but extensions to other imaging modalities and structures are also discussed. These images and associated ground-truth data can be used to develop, test and validate image processing and analysis algorithms or to train and validate machine learning approaches. The use of synthetic images has the advantage that neither access to an imaging system, nor to human participants is necessary for development.

FACE RECOGNITION USING FUZZY INFERENCE SYSTEM

2009 ◽  
Vol 8 (3) ◽  
pp. 887-897
Author(s):  
Vishal Paika ◽  
Er. Pankaj Bhambri
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Facial Features ◽  
Facial Appearance ◽  
Feature Points ◽  
Inference System ◽  
Detection Techniques ◽  
The Face ◽  
Hair Style ◽  
Facial Images

The face is the feature which distinguishes a person. Facial appearance is vital for human recognition. It has certain features like forehead, skin, eyes, ears, nose, cheeks, mouth, lip, teeth etc which helps us, humans, to recognize a particular face from millions of faces even after a large span of time and despite large changes in their appearance due to ageing, expression, viewing conditions and distractions such as disfigurement of face, scars, beard or hair style. A face is not merely a set of facial features but is rather but is rather something meaningful in its form.In this paper, depending on the various facial features, a system is designed to recognize them. To reveal the outline of the face, eyes, ears, nose, teeth etc different edge detection techniques have been used. These features are extracted in the term of distance between important feature points. The feature set obtained is then normalized and are feed to artificial neural networks so as to train them for reorganization of facial images.

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