Face recognition algorithms as models of human face processing

2002 ◽  
Author(s):  
A.J. O'Toole ◽  
Yi Cheng ◽  
P.J. Phillips ◽  
B. Ross ◽  
H.A. Wild
Keyword(s):  
Face Recognition ◽  
Face Processing ◽  
Human Face ◽  
Recognition Algorithms

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.

Introduction

2015 ◽  
pp. 3-7
Keyword(s):  
Pattern Recognition ◽  
Face Recognition ◽  
New Technologies ◽  
Similarity Measures ◽  
Growing Up ◽  
Human Face ◽  
Problem Statement ◽  
Video Images ◽  
Recognition Algorithms

Face recognition is a growing-up branch of pattern recognition in the context of image and vision. Conferences have arisen and brand new technologies have been coming to light providing more and more accurate recognition rates. But what is face recognition? The problem statement could be formulated this way: “Given still or video images of a scene, identify or verify one or more persons in the scene using a stored database of faces” [1]. Face recognition branch is core inasmuch the applications involving recognition algorithms for human face are aimed at different applications such as biometrics, authentication, identification of suspects. This chapter offers an overview of what are similarity and similarity measures.

Face Recognition with Active Appearance Model (AAM)

2013 ◽  
pp. 1-22
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.

scholarly journals Robot Face Memorability is Affected by Uncanny Appearance

2021 ◽  
Author(s):  
Allie Geiger ◽  
Benjamin Balas
Keyword(s):  
Face Recognition ◽  
Face Processing ◽  
Memory Task ◽  
Human Robot Interaction ◽  
Study Phase ◽  
Human Face ◽  
Human Face Recognition ◽  
Social Agents ◽  
Face Images ◽  
Human Faces

Human face recognition is influenced by various social and environmental constructs. For example, both age and race can affect the likelihood that a human face will be correctly recalled. Interestingly, general face appearance (i.e. friendly or untrustworthy faces) can also influence memorability. As human-robot interaction (HRI) becomes more commonplace, understanding what factors influence face recognition for non-human social agents is increasingly important. In particular, while there is a growing literature comparing the recognition of real human faces to computer-generated face images, comparisons between human face processing and robot face processing are largely unexplored. Here, we examined how the uncanny/eeriness of robot-faces affects memorability by using a 2AFC old/new task with various robot faces. Participants rated robot and human faces on perceived uncanniness during a study phase and were subsequently given a surprise memory task with only a fraction of the previously-encountered robot faces. Our results suggest that robots who are rated as more uncanny are more memorable than those that do not elicit the eerie feelings that correspond with uncanny faces: The more uncanny the robot face, the more accurately and quickly they were recalled. We discuss these results in the context of the design of social agents for HRI and also vis-a-vis theories of human face recognition and memory.

COMPARISON OF FACE RECOGNITION ALGORITHMS FOR SURVEILLANCE OF CHILDREN USING OPENCV.

2021 ◽  
Vol 1 (10) ◽  
pp. 328-332
Author(s):  
Sourabh Kumar ◽  
Bhaskar Kapoor Kapoor
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Security System ◽  
Human Face ◽  
Security Measures ◽  
Recognition Algorithms ◽  
The Face ◽  
Haar Cascade

Proposing a security system for surveillance of home alone children for safety purpose and send an alert to the register mobile number if some kind of intrusion is detected. I have used Viola-Jones algorithm to detect human face from the live camera and then frame is resized then resized image is processed by the Local Binary Pattern Histograms (LBPH) algorithm and save the model in a YML file and then it is implemented on live cam feed in which the algorithm will detect the face and if some unknown face has been identified it will trigger a notification to the registered mobile number using a python library named [Pywhatkit] so the user can perform security measures. Keywords: Face recognition, Open-CV, HAAR cascade, face recognition.

A LOW-COMPUTATION APPROACH FOR HUMAN FACE RECOGNITION

2012 ◽  
Vol 26 (06) ◽  
pp. 1256015 ◽  
Author(s):  
WEI-LI FANG ◽  
YING-KUEI YANG ◽  
JUNG-KUEI PAN
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Face Image ◽  
Experimental Results ◽  
Human Face ◽  
Human Face Recognition ◽  
Computation Cost ◽  
Recognition Algorithms

Several 2DPCA-based face recognition algorithms have been proposed hoping to achieve the goal of improving recognition rate while mostly at the expense of computation cost. In this paper, an approach named SI2DPCA is proposed to not only reduce the computation cost but also increase recognition performance at the same time. The approach divides a whole face image into smaller sub-images to increase the weight of features for better feature extraction. Meanwhile, the computation cost that mainly comes from the heavy and complicated operations against matrices is reduced due to the smaller size of sub-images. The reduced amount of computation has been analyzed and the integrity of sub-images has been discussed thoroughly in the paper. The experiments have been conducted to make comparisons among several better-known approaches and SI2DPCA. The experimental results have demonstrated that the proposed approach works well on reaching the goals of reducing computation cost and improving recognition performance simultaneously.

Face Recognition by Humans and Machines: Three Fundamental Advances from Deep Learning

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Alice J. O’Toole ◽  
Carlos D. Castillo
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Face Processing ◽  
Scientific Progress ◽  
Population Coding ◽  
Publication Date ◽  
Human Face ◽  
Learning Models ◽  
Deep Networks ◽  
High Level

Deep learning models currently achieve human levels of performance on real-world face recognition tasks. We review scientific progress in understanding human face processing using computational approaches based on deep learning. This review is organized around three fundamental advances. First, deep networks trained for face identification generate a representation that retains structured information about the face (e.g., identity, demographics, appearance, social traits, expression) and the input image (e.g., viewpoint, illumination). This forces us to rethink the universe of possible solutions to the problem of inverse optics in vision. Second, deep learning models indicate that high-level visual representations of faces cannot be understood in terms of interpretable features. This has implications for understanding neural tuning and population coding in the high-level visual cortex. Third, learning in deep networks is a multistep process that forces theoretical consideration of diverse categories of learning that can overlap, accumulate over time, and interact. Diverse learning types are needed to model the development of human face processing skills, cross-race effects, and familiarity with individual faces. Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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