Fan and Non-Fan Recollection of Faces in Fandom-Related Art and Costumes

2018 ◽  
Vol 18 (1-2) ◽  
pp. 224-229
Author(s):  
Stephen Reysen ◽  
Courtney N. Plante ◽  
Sharon E. Roberts ◽  
Kathleen C. Gerbasi
Keyword(s):  
Face Recognition ◽  
Animal Art ◽  
Human Faces

Abstract We compared face recognition of humans and fandom-themed characters (art and costumes) between a sample of furries (fans of anthropomorphic animal art) and non-furries. Participants viewed images that included humans, drawn anthropomorphic animals, and anthropomorphic animal costumes, and were later tested on their ability to recognize faces from a subset of the viewed images. While furries and non-furries did not differ in their recollection of human faces, furries showed significantly better memory for faces in furry-themed artwork and costumes. The results are discussed in relation to own-group bias in face recognition.

The Adaboost Algorithm Applied in ATM Automatic Identification System

2013 ◽  
Vol 753-755 ◽  
pp. 2941-2944
Author(s):  
Ming Hui Zhang ◽  
Yao Yu Zhang
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Computing Time ◽  
Automatic Identification ◽  
Identification System ◽  
Human Face ◽  
Detection Experiment ◽  
Adaboost Algorithm ◽  
Face Features ◽  
Human Faces

Seeing that human face features are unique, an increasing number of face recognition algorithms on existing ATM are proposed. Since face detection is a primary link of face recognition, our system adopts AdaBoost algorithm which is based on face detection. Experiment results demonstrated that the computing time of face detection using this algorithm is about 70ms, and the single and multiple human faces can be effectively measured under well environment, which meets the demand of the system.

Algorithmic Analysis of Automatic Attendance System using Facial Recognition

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Face Recognition ◽  
Daily Life ◽  
Real Life ◽  
Student Attendance ◽  
Surveillance Camera ◽  
Daily Life Activities ◽  
The Face ◽  
Algorithmic Analysis ◽  
Human Faces ◽  
Face Biometrics

Attendance management can become a tedious task for teachers if it is performed manually.. This problem can be solved with the help of an automatic attendance management system. But validation is one of the main issues in the system. Generally, biometrics are used in the smart automatic attendance system. Managing attendance with the help of face recognition is one of the biometric methods with better efficiency as compared to others. Smart Attendance with the help of instant face recognition is a real-life solution that helps in handling daily life activities and maintaining a student attendance system. Face recognition-based attendance system uses face biometrics which is based on high resolution monitor video and other technologies to recognize the face of the student. In project, the system will be able to find and recognize human faces fast and accurately with the help of images or videos that will be captured through a surveillance camera. It will convert the frames of the video into images so that our system can easily search that image in the attendance database.

scholarly journals Face Recognition Using Neural Networks

2011 ◽  
pp. 76-79
Author(s):  
V. Ramya ◽  
G. Sivashankari
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Recognition Performance ◽  
Back Propagation ◽  
Rbf Networks ◽  
End Point ◽  
Novel Approach ◽  
Wide Variability ◽  
Human Faces ◽  
Face Localization

Face recognition from the images is challenging due to the wide variability of face appearances and the complexity of the image background. This paper proposes a novel approach for recognizing the human faces. The recognition is done by comparing the characteristics of the new face to that of known individuals. It has Face localization part, where mouth end point and eyeballs will be obtained. In feature Extraction, Distance between eyeballs and mouth end point will be calculated. The recognition is performed by Neural Network (NN) using Back Propagation Networks (BPN) and Radial Basis Function (RBF) networks. The recognition performance of the proposed method is tabulated based on the experiments performed on a number of images.

Face Recognition with Active Appearance Model (AAM)

2013 ◽  
pp. 1124-1144 ◽  
Author(s):  
Patrycia Barros de Lima Klavdianos ◽  
Lourdes Mattos Brasil ◽  
Jairo Simão Santana Melo
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Face Recognition ◽  
Face Processing ◽  
Research Field ◽  
Appearance Model ◽  
Active Appearance Model ◽  
Human Face ◽  
Active Appearance ◽  
Human Faces

Recognition of human faces has been a fascinating subject in research field for many years. It is considered a multidisciplinary field because it includes understanding different domains such as psychology, neuroscience, computer vision, artificial intelligence, mathematics, and many others. Human face perception is intriguing and draws our attention because we accomplish the task so well that we hope to one day witness a machine performing the same task in a similar or better way. This chapter aims to provide a systematic and practical approach regarding to one of the most current techniques applied on face recognition, known as AAM (Active Appearance Model). AAM method is addressed considering 2D face processing only. This chapter doesn’t cover the entire theme, but offers to the reader the necessary tools to construct a consistent and productive pathway toward this involving subject.

Biometrical Processing of Faces in Security and Forensics

2010 ◽  
pp. 79-103
Author(s):  
Pawel T. Puslecki
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Face Processing ◽  
Facial Features ◽  
Comprehensive Description ◽  
Facial Processing ◽  
Human Faces

The aim of this chapter is the overall and comprehensive description of the machine face processing issue and presentation of its usefulness in security and forensic applications. The chapter overviews the methods of face processing as the field deriving from various disciplines. After a brief introduction to the field, the conclusions concerning human processing of faces that have been drawn by the psychology researchers and neuroscientists are described. Then the most important tasks related to the computer facial processing are shown: face detection, face recognition and processing of facial features, and the main strategies as well as the methods applied in the related fields are presented. Finally, the applications of digital biometrical processing of human faces are presented.

scholarly journals Reconstructed Error and Linear Representation Coefficients Restricted by l1-Minimization for Face Recognition under Different Illumination and Occlusion

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Xuegang Wu ◽  
Bin Fang ◽  
Yuan Yan Tang ◽  
Xiaoping Zeng ◽  
Changyuan Xing
Keyword(s):  
Face Recognition ◽  
Linear Combination ◽  
High Performance ◽  
Linear Subspace ◽  
Linear Representation ◽  
Optimization Method ◽  
Reconstruction Error ◽  
Equivalent Transformation ◽  
L1 Minimization ◽  
Human Faces

The problem of recognizing human faces from frontal views with varying illumination, occlusion, and disguise is a great challenge to pattern recognition. A general knowledge is that face patterns from an objective set sit on a linear subspace. On the proof of the knowledge, some methods use the linear combination to represent a sample in face recognition. In this paper, in order to get the more discriminant information of reconstruction error, we constrain both the linear combination coefficients and the reconstruction error by l1-minimization which is not apt to be disturbed by outliners. Then, through an equivalent transformation of the model, it is convenient to compute the parameters in a new underdetermined linear system. Next, we use an optimization method to get the approximate solution. As a result, the minimum reconstruction error has contained much valuable discriminating information. The gradient of this variable is measured to decide the final recognition. The experiments show that the recognition protocol based on the reconstruction error achieves high performance on available databases (Extended Yale B and AR Face database).

MCE-BASED FACE RECOGNITION

2001 ◽  
Vol 15 (08) ◽  
pp. 1311-1327 ◽  
Author(s):  
LIANG-HUA CHEN ◽  
SHAO-HUA DENG ◽  
HONG-YUAN LIAO
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Face Detection ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Classification Error ◽  
Complex Scenes ◽  
Minimum Classification Error ◽  
Complete Procedure ◽  
Human Faces

This paper proposes a complete procedure for the extraction and recognition of human faces in complex scenes. The morphology-based face detection algorithm can locate multiple faces oriented in any direction. The recognition algorithm is based on the minimum classification error (MCE) criterion. In our work, the minimum classification error formulation is incorporated into a multilayer perceptron neural network. Experimental results show that our system is robust to noisy images and complex background.

3-D FACE MODELING AND ITS APPLICATIONS

1996 ◽  
Vol 10 (05) ◽  
pp. 491-520 ◽  
Author(s):  
THOMAS S. HUANG ◽  
LI-AN TANG
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Computer Animation ◽  
Video Sequence ◽  
Human Face ◽  
Face Modeling ◽  
Human Face Recognition ◽  
Preliminary Results ◽  
Human Faces ◽  
Sequence Compression

This paper describes some issues in building a 3-D human face modeling system which mainly consists of three parts: • Modeling human faces; • Analyzing facial motions; • Synthesizing facial expressions. A variety of techniques developed for this system are described in detail in this paper. Some preliminary results of applying this system to computer animation, video sequence compression and human face recognition are also shown.

scholarly journals Security Implementation by Machine Learning Based Face Recognization with SVM

2021 ◽  
pp. 214-218
Author(s):  
Apurva Yawalikar ◽  
U. W. Hore
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Detection System ◽  
Object Class ◽  
Detection Systems ◽  
The Face ◽  
Object Class Detection ◽  
Human Faces ◽  
Work Done

Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given. As per the various face detection system seen various work done onto the detection with various way. In existing this are get evaluate with the HOG with SVM, which will help us to get the exact value so that it is necessary to implement the system which will more effective and advance. As per the face detection seen there are various face detection systems are implemented. Determining face is easy but recognition is quite typical so that we are proposed machine learning based face recognition with SVM which helps to determine and detect the faces So the proposed system will get integrated with highly efficient and effective SVM model for face recognition. The proposed methodology will help us to implement the face based security implementation in any security system like door lock, mobile screen lock etc.

scholarly journals A Review on Online Classroom Attendance Marking System Using Face Recognition, Python, Computer Vision, and Digital Image Processing

2021 ◽  
Vol 9 (12) ◽  
pp. 2342-2345
Author(s):  
Nandkishor Satpute
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Face Recognition ◽  
Processing Method ◽  
Online Classroom ◽  
Detection Algorithms ◽  
The Neural Network ◽  
Network Concept ◽  
The Face ◽  
Human Faces

Abstract: The face is that the identity of someone. The tactic to appear out this physical feature has seen an exquisite change since the advent of the image processing method. Attendance is monitored in every school, college and library. The regular method for attendance is for teachers to call student name & mark attendance. Nowadays, AI has been explored for computer vision-related applications. So, we use the neural network concept in Face recognition for automatically attendance marking systems. This project will perform the face recognition and face detection algorithms, to generate the computer systems strength of acquiring and recognizing human faces fast, accurately, and precisely in live streams so that the systems can be used in the marking attendance

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