Intra-Class Threshold Selection in Face Space Using Set Estimation Technique

2010 ◽  
pp. 69-84
Author(s):  
Madhura Datta ◽  
C. A. Murthy
Keyword(s):  
Face Recognition ◽  
Real Life ◽  
Recognition System ◽  
Decision Threshold ◽  
Threshold Selection ◽  
Query Image ◽  
Closed Set ◽  
Set Estimation ◽  
Open Test ◽  
Open Question

Most of the conventional face recognition algorithms are dissimilarity based, and for the sake of open and closed set classification one needs to put a proper threshold on the dissimilarity value. On the basis of the decision threshold, a biometric recognition system should be in a position to accept the query image as client or reject him as imposter. However, the selection of proper threshold of a given class in a dataset is an open question, as it is related to the difficulty levels dictated in face recognition problems. In this chapter, the authors have introduced a novel thresholding technique for a real life scenario where the query face image may not be present in the training database, i.e. often referred by the biometric researchers as the open test identification. The theoretical basis of the thresholding technique and its corresponding verification on several datasets has been successfully demonstrated in the article. The proposed threshold selection is based on statistical method of set estimation and is guided by minimal spanning tree. It has been found that the proposed technique performs better than the ROC curve based threshold selection mechanism.

scholarly journals Realtime Face-Mask Detection on Raspberry Kit

2021 ◽  
Vol 9 (8) ◽  
pp. 2076-2083
Author(s):  
Kalyan Chakravarthi. M
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Real Life ◽  
Face Mask ◽  
Recognition System ◽  
Primary Concern ◽  
Public Places ◽  
Life Problems ◽  
Promising Area ◽  
The Face

Abstract: Recognition from faces is a popular and significant technology in recent years. Face alterations and the presence of different masks make it too much challenging. In the real-world, when a person is uncooperative with the systems such as in video surveillance then masking is further common scenarios. For these masks, current face recognition performance degrades. Still, difficulties created by masks are usually disregarded. Face recognition is a promising area of applied computer vision . This technique is used to recognize a face or identify a person automatically from given images. In our daily life activates like, in a passport checking, smart door, access control, voter verification, criminal investigation, and many other purposes face recognition is widely used to authenticate a person correctly and automatically. Face recognition has gained much attention as a unique, reliable biometric recognition technology that makes it most popular than any other biometric technique likes password, pin, fingerprint, etc. Many of the governments across the world also interested in the face recognition system to secure public places such as parks, airports, bus stations, and railway stations, etc. Face recognition is one of the well-studied real-life problems. Excellent progress has been done against face recognition technology throughout the last years. The primary concern to this work is about facial masks, and especially to enhance the recognition accuracy of different masked faces. A feasible approach has been proposed that consists of first detecting the facial regions. The occluded face detection problem has been approached using Cascaded Convolutional Neural Network (CNN). Besides, its performance has been also evaluated within excessive facial masks and found attractive outcomes. Finally, a correlative study also made here for a better understanding.

scholarly journals FACE RECOGNITION FOR THE AUTOMATIC ATTENDANCE MANAGEMENT SYSTEM

2020 ◽  
pp. 116-120
Author(s):  
N.Ramya Sri ◽  
D.Manasa ◽  
N.Ramya ◽  
Sk.Naveed
Keyword(s):  
Face Recognition ◽  
Real Life ◽  
Personal Characteristics ◽  
Recognition System ◽  
Personal Identification ◽  
Identification System ◽  
Information Process ◽  
White Balance ◽  
Face Recognition System ◽  
The Face

Face Recognition is a currently developing technology with multiple real- life applications. The goal of this Thesis is to develop a complete Face Recognition system. The system uses Convolutional Neural Networks in order to extract relevant facial features. These features allow to compare faces between them in an efficient way. The system can be trained to recognize a set of people, and to learn in an on-line way, by integrating the new people. Face recognition system is one of the biometric information process its applicability is easier and working range is larger than others .The face recognition is live acquired images without any application field in mind .process utilized in the system are White Balance correction ,skin like region segmentation .facial feature extraction and face image extraction on a face Candidate .The face one of the easiest ways to distinguish the individual identify each other .Face recognition is a personal identification system that uses personal characteristics of a person to identify the person’s identify. KEYWORDS:-Face Recognition, Facial Attendance, Automatic Attendance, Face Detection.

scholarly journals Low Resolution Face Recognition System Performance for Basic Feature Space Methods with Various Illumination Normalization Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 4261-4264
Keyword(s):  
Face Recognition ◽  
Real Life ◽  
Feature Space ◽  
Basic Feature ◽  
Recognition System ◽  
Surveillance Systems ◽  
Face Database ◽  
Low Resolution ◽  
Illumination Normalization ◽  
Low Resolution Images

The Face recognition research identified with the field of automated surveillance systems in real life application has pulled in more consideration today with extensive use of vision cameras in biometrics and surveillance applications, globally. The small size images or poor quality images are generally Low Resolution images. Authenticating faces, with variation in pose, illumination, disguise and more, from such Low-resolution images, is the main purpose of Low Resolution Face Recognition (LR FR) system. We have assessed the LR FR system for various basic feature space techniques like PCA, LDA and Fisherface. Different illumination normalization techniques are applied on the cropped Yale face database prior to feature extraction and identification. In our work the low-resolution images, of size 32x32, are the down-sampled versions high-resolution facial images from cropped Yale face database. Our experiment demonstrates the encouraging performance, with recognition accuracy as 97.43%, on Low Resolution, Low quality face images.

scholarly journals Man in the Middle of Face Recognition System: using Skin Color and Template

2019 ◽  
Vol 9 (2) ◽  
pp. 2601-2610
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Skin Color ◽  
Initial Step ◽  
Recognition System ◽  
Feedback Mechanism ◽  
Query Image ◽  
Face Recognition System ◽  
The Face ◽  
Compensation Technique

During last 10 years people are very much attracted to face recognition systems and they are very much eager to solve the issues related to face recognition system. It helped them very much in the field of electronics and uses over pattern unlocking and password entering system. There are numerous applications as for security, affectability and mystery. Detection of a face is the most significant and initial step of recognition framework. This article demonstrates a new method to face recognition system using color and template of an image. Whatever the background it may go to be, our system will detect the face, which is an important stage for face detection. The pictures utilized in this framework for Face detection are the color images, while the images used for the Face Recognition are the Gray images which are converted from color pictures. The illumination compensation technique is applied on all the images for removing the effect of light. The Red, Green, and Blue values of each pixel will be converted to YCbCr space. Based on the probability of each pixel in terms of Cb, Cr values, we extract the skin pixels from the query image,. The positive probability shows a “skin pixel”, while the negative probability shows “not a skin pixel”. Finally the face is projected. In face recognition, we used 4 templates of different sizes for Gabor image content extraction. Finally we employed the relevance feedback mechanism to retrieve the most similar images. If the user did not satisfy with the given results he can give the correct images to the system from the displayed images. Exploratory outcomes demonstrate that the demonstrated system is adequate to recognize face of a human face in a picture with an exactness of 94%.

scholarly journals Design of face recognition system based on CNN

2020 ◽  
Vol 1601 ◽  
pp. 052011
Author(s):  
Yong Li ◽  
Zhe Wang ◽  
Yang Li ◽  
Xu Zhao ◽  
Hanwen Huang
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Face Recognition System

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

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