Stability from Variation: The Case of Face Recognition the M.D. Vernon Memorial Lecture

1994 ◽  
Vol 47 (1) ◽  
pp. 5-28 ◽  
Author(s):  
Vicki Bruce
Keyword(s):  
Face Recognition ◽  
Visual Perception ◽  
Facial Expressions ◽  
Continuous Variation ◽  
Central Problem ◽  
Memorial Lecture ◽  
Face Identification ◽  
The Face ◽  
The Way

A theme running through M.D. Vernon's discussions of visual perception was the key question of how we perceive a stable world despite continuous variation. The central problem in face identification is how we build stable representations from exemplars that vary, both rigidly and non-rigidly, from instant to instant and from encounter to encounter. Experiments reveal that people are rather poor at generalizing from one exemplar of a face to another (e.g. from one photograph to another showing a different view or expression) yet highly accurate at encoding precise details of faces within the range shown by several slightly different exemplars. Moreover, provided instructions do not encourage subjects explicitly to attend to the way that different exemplars vary, faces are retained in a way that enhances familiarity of the prototype of the set, even if this was not presented for study. It is suggested that our usual encounters with continuous variations of facial expressions, angles, and lightings provide the conditions necessary to establish stable representations of individuals within an overall category (the face) where all members share the same overall structure. These observations about face recognition would probably not have come as any great surprise to Maggie Vernon, many of whose more general observations about visual perception anticipated such conclusions.

scholarly journals Face Recognition Model Using Back Propagation

2018 ◽  
Vol 7 (3.34) ◽  
pp. 237
Author(s):  
R Aswini Priyanka ◽  
C Ashwitha ◽  
R Arun Chakravarthi ◽  
R Prakash
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Face Identification ◽  
Identification System ◽  
Scientific World ◽  
The Face ◽  
Deep Learning Neural Network

In scientific world, Face recognition becomes an important research topic. The face identification system is an application capable of verifying a human face from a live videos or digital images. One of the best methods is to compare the particular facial attributes of a person with the images and its database. It is widely used in biometrics and security systems. Back in old days, face identification was a challenging concept. Because of the variations in viewpoint and facial expression, the deep learning neural network came into the technology stack it’s been very easy to detect and recognize the faces. The efficiency has increased dramatically. In this paper, ORL database is about the ten images of forty people helps to evaluate our methodology. We use the concept of Back Propagation Neural Network (BPNN) in deep learning model is to recognize the faces and increase the efficiency of the model compared to previously existing face recognition models.   

scholarly journals Novel Descriptors for Effective Recognition of Face and Facial Expressions

2020 ◽  
Vol 34 (5) ◽  
pp. 521-530
Author(s):  
Farid Ayeche ◽  
Adel Alti
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Support Vector Machine Classifier ◽  
Recognition Rate ◽  
Face Image ◽  
Support Vector ◽  
Expression Recognition ◽  
The Face ◽  
Public Datasets ◽  
Edge Response

In this paper, we present a face recognition approach based on extended Histogram Oriented Gradient (HOG) descriptors to extract the facial expressions features allowing classifying the faces and facial expressions. The approach is based on determining the different directional codes on the face image based on edge response values to define the feature vector from the face image. Its size is reduced to improve the performance of the SVM (Support Vector Machine) classifier. Experiments are conducted using two public datasets: JAFFE for facial expression recognition and YALE for face recognition. Experimental results show that the proposed descriptor achieves recognition rate of 92.12% and execution time ranging from 0.4s to 0.7s in all evaluated databases compared with existing works. Experiments demonstrate and confirm both the effectiveness and the efficiency of the proposed descriptor.

scholarly journals ROBUST REPRESENTATION OF 3D FACES FOR RECOGNITION

2006 ◽  
Vol 20 (08) ◽  
pp. 1159-1186 ◽  
Author(s):  
A. BELÉN MORENO ◽  
ÁNGEL SÁNCHEZ ◽  
ENRIQUE FRÍAS-MARTÍNEZ
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Facial Recognition ◽  
Recognition Rate ◽  
Relevant Information ◽  
Large Set ◽  
Discrimination Power ◽  
3D Face ◽  
The Face ◽  
The Difference

Automatic face recognition is becoming increasingly important due to the security applications derived from it. Although the facial recognition problem has focused on 2D images, recently, due to the proliferation of 3D scanning hardware, 3D face recognition has become a feasible application. This 3D approach does not need any color information. In this way, it has the following main advantages in comparison to more traditional 2D approaches: (1) being robust under lighting variations and (2) providing more relevant information. In this paper we present a new 3D facial model based on the curvature properties of the surface. Our system is able to detect the subset of the characteristics of the face with higher discrimination power from a large set. The robustness of the model is tested by comparing recognition rates using both controlled and noncontrolled environments regarding facial expressions and facial rotations. The difference between the recognition rates of the two environments of only 5% proves that the model has a high degree of robustness against pose and facial expressions. We consider that this robustness is enough to implement facial recognition applications, which can achieve up to 91% correct recognition rate. A publish 3D face database containing face rotations and expressions has been created to achieve the recognition experiments.

scholarly journals Evaluation of 3d facial paralysis using fuzzy logic

2018 ◽  
Vol 7 (4) ◽  
pp. 2325
Author(s):  
Banita . ◽  
Dr Poonam Tanwar
Keyword(s):  
Face Recognition ◽  
Facial Paralysis ◽  
Facial Expressions ◽  
Research Work ◽  
Laser Acupuncture ◽  
Medical Terminology ◽  
Face Model ◽  
The Face ◽  
The Right

Face recognition are of great interest to researchers in terms of Image processing and Computer Graphics. In recent years, various factors become popular which clearly affect the face model. Which are ageing, universal facial expressions, and muscle movement. Similarly in terms of medical terminology the facial paralysis can be peripheral or central depending on the level of motor neuron lesion which can be below the nucleus of the nerve or supra nuclear. The various medical therapy used for facial paralysis are electroaccupunture, electrotherapy, laser acupuncture, manual acupuncture which is a traditional form of acupuncture. Imaging plays a great role in evaluation of degree of paralysis and also for faces recognition. There is a wide research in terms of facial expressions and facial recognition but limited research work is available in facial paralysis. House- Brackmann Grading system is one of the simplest and easiest method to evaluate the degree of facial paralysis. During evaluation common facial expressions are recorded and are further evaluated by considering the focal points of the left or the right side of the face. This paper presents the classification of face recognition and its respective fuzzy rules to remove uncertainty in the result after evaluation of facial paralysis.  

scholarly journals Aplikasi Screen Lock pada Smartphone Menggunakan Identifikasi Wajah dengan Menerapkan Pointwise

2015 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Andi Widiyanto ◽  
Bintang Dian Mahardika
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Success Rate ◽  
Face Image ◽  
Color Images ◽  
Face Identification ◽  
Test Results ◽  
Image Capture ◽  
The Face ◽  
True Color

Penerapan identifikasi wajah (face recognition) telah diterapkan pada komputer, laptop atau alat-alat lain yang memang dikhususkan untuk identifikasi wajah. Perkembangan smartphone khususnya android berkembang dengan cepat. Untuk menjaga keamanan supaya hanya dapat digunakan oleh pemilik telah disediakan dengan PIN, phone code, pola geser titik sentuh layar. Aplikasi identifikasi wajah digunakan sebagai pengganti PIN atau code phone pada smartphone android dibutuhkan sebagai proteksi supaya hanya pemiliknya saja yang dapat menggunakannya. Supaya proses identifikasi wajah pemilik lebih mudah perlu dilakukan konversi dari gambar true color ke grayscale proses yang digunakan adalah pointwise. Aplikasi face recognition yang dibangun membutuhkan training wajah pemilik dengan 6 pose wajah yang disimpan, kemudian akan dibandingkan dengan identifikasi wajah saat aplikasi digunakan. Hasil pengujian menunjukkan bahwa tingkat keberhasilan antara 70% - 90%. Jarak antara wajah dan kamera serta tingkat kecerahan cahaya mempengaruhi hasil dari identifikasi wajah. Tingkat keberhasilan identifikasi wajah ditentukan oleh pengambilan image, pemrosesan image, dan perhitungan dengan PCA (eigenface).Face recognition has been implemented on a computer, laptop or other device tool which is dedicated for face identification. Developments in particular android smartphones growing rapidly. To maintain the security that can only be used by owners have been provided with a PIN, phone code, pattern shear point touch screen. Face recognition application used as a substitute for or a PIN code on the phone android smartphone needed as protection so only the owner who can use it. So that the process of identification of the owner's face needs to be done easier conversion of true color images into grayscale process used is pointwise. Face recognition application that is built requires owners face training with 6 face pose saved , then will be compared with the face identification when the application is used . The test results showed that the success rate of between 70 % - 90 %. The distance between the face and the camera and the brightness of light affect the results of face identification. The success rate is determined by identifying the face image capture, image processing, and computation with PCA eigenface.

scholarly journals Expression Invariant Features for Face Recognition

2019 ◽  
Vol 9 (2) ◽  
pp. 2012-2016
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Facial Expression ◽  
Facial Expressions ◽  
Personal Computer ◽  
The Other ◽  
Machine Learning Methods ◽  
Face Images ◽  
Invariant Features ◽  
The Face

Personal Computer sourced Face Recognition has been a sophisticated and well-found technique which is being rationally utilized for most of the authenticated cases. In reality, there is a number of situations where the expressions of the face will be different. We are here able to instinctively detect the five universal expressions: smile, sadness, anger, surprise, neutral by studying face geometry by determining which type of facial expression has been carried out. Using some facial data with variant expressions. We hereby made some experimentations to calculate the accuracies of some machine learning methods by making some changes in the face images such as a change in expressions, which at last needed for training and recognition identifiers. Our objective is to take the features of neutral facial expressions and add them with the other expressive face images like smiling, angry, sadness to improve the accuracy.

scholarly journals An Separation of Different Facial Expression Recognition System

2019 ◽  
Vol 8 (6S4) ◽  
pp. 438-441
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Facial Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Digital Data ◽  
Expression Recognition ◽  
Share Data ◽  
The Face ◽  
Facial Recognition System

Face recognition is one of the hot topics in the current world and one of the popular topics of computer studies. Today face recognition in the network society and access to digital data is gaining more attention. The facial recognition system technology is a biometric assessment of a human's face. There are many facial recognition techniques that are intended depending on facial expressions extraction, one of which is 3D facial recognition, as well as their fusion,is difficult. During preprocessing measures for picture recognition to remove only expression-specific characteristics from the face and prevent their issues with a convolution neural network. We can also use some theorems such as LBP and Taylor's theorem to model face recognition. In particular, for cloud robots, we can also use this facial recognition on robots. The robot can perform functions and share data between servers and devices. Seven fundamental expressions are used to identify and classify: happiness, shock, fear, disgust, sadness, rage, and a neutral condition. Until now, the recognition rate is quite up to the expectation stage, but it still tries to enhance. To enhance the recognition frequency of facial image recognition, feelings are chosen by the vibrant Bayesian network technique to depict the development of facial awareness in addition to various emotional operations of facial expressions. The ICCA techniques involve various multivariate sets of distinct facial features that could be eyes, nose, and mouth.

scholarly journals System for Face Recognition under Different Facial Expressions Using a New Associative Hybrid Model Amαβ-KNN for People with Visual Impairment or Prosopagnosia

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 578 ◽  
Author(s):  
Moisés Márquez-Olivera ◽  
Antonio-Gustavo Juárez-Gracia ◽  
Viridiana Hernández-Herrera ◽  
Amadeo-José Argüelles-Cruz ◽  
Itzamá López-Yáñez
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Hybrid Model ◽  
Integrated System ◽  
K Nearest Neighbors ◽  
The People ◽  
Proposed Model ◽  
The Face ◽  
Controlled Environments ◽  
The Individual

Face recognition is a natural skill that a child performs from the first days of life; unfortunately, there are people with visual or neurological problems that prevent the individual from performing the process visually. This work describes a system that integrates Artificial Intelligence which learns the face of the people with whom the user interacts daily. During the study we propose a new hybrid model of Alpha-Beta Associative memories (Amαβ) with Correlation Matrix (CM) and K-Nearest Neighbors (KNN), where the Amαβ-CMKNN was trained with characteristic biometric vectors generated from images of faces from people who present different facial expressions such as happiness, surprise, anger and sadness. To test the performance of the hybrid model, two experiments that differ in the selection of parameters that characterize the face are conducted. The performance of the proposed model was tested in the databases CK+, CAS-PEAL-R1 and Face-MECS (own), which test the Amαβ-CMKNN with faces of subjects of both sexes, different races, facial expressions, poses and environmental conditions. The hybrid model was able to remember 100% of all the faces learned during their training, while in the test in which faces are presented that have variations with respect to those learned the results range from 95.05% in controlled environments and 86.48% in real environments using the proposed integrated system.

scholarly journals Impairment effect of infantile coloration on face discrimination in chimpanzees

2021 ◽  
Vol 8 (11) ◽  
Author(s):  
Yuri Kawaguchi ◽  
Koyo Nakamura ◽  
Masaki Tomonaga ◽  
Ikuma Adachi
Keyword(s):  
Face Recognition ◽  
Discrimination Performance ◽  
Matching To Sample ◽  
Face Identification ◽  
Age Class ◽  
The Face ◽  
Face Stimuli ◽  
Infant Faces

Impaired face recognition for certain face categories, such as faces of other species or other age class faces, is known in both humans and non-human primates. A previous study found that it is more difficult for chimpanzees to differentiate infant faces than adult faces. Infant faces of chimpanzees differ from adult faces in shape and colour, but the latter is especially a salient cue for chimpanzees. Therefore, impaired face differentiation of infant faces may be due to a specific colour. In the present study, we investigated which feature of infant faces has a greater effect on face identification difficulty. Adult chimpanzees were tested using a matching-to-sample task with four types of face stimuli whose shape and colour were manipulated as either infant or adult one independently. Chimpanzees' discrimination performance decreased as they matched faces with infant coloration, regardless of the shape. This study is the first to demonstrate the impairment effect of infantile coloration on face recognition in non-human primates, suggesting that the face recognition strategies of humans and chimpanzees overlap as both species show proficient face recognition for certain face colours.

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