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.

Face Perception in Face Transplant Patients

2019 ◽  
Vol 35 (05) ◽  
pp. 525-533
Author(s):  
Evrim Gülbetekin ◽  
Seda Bayraktar ◽  
Özlenen Özkan ◽  
Hilmi Uysal ◽  
Ömer Özkan
Keyword(s):  
Face Recognition ◽  
Object Recognition ◽  
Face Processing ◽  
Recognition Performance ◽  
Discrimination Performance ◽  
Object Discrimination ◽  
Face Transplantation ◽  
Transplant Patients ◽  
Facial Surgery ◽  
The Face

AbstractThe authors tested face discrimination, face recognition, object discrimination, and object recognition in two face transplantation patients (FTPs) who had facial injury since infancy, a patient who had a facial surgery due to a recent wound, and two control subjects. In Experiment 1, the authors showed them original faces and morphed forms of those faces and asked them to rate the similarity between the two. In Experiment 2, they showed old, new, and implicit faces and asked whether they recognized them or not. In Experiment 3, they showed them original objects and morphed forms of those objects and asked them to rate the similarity between the two. In Experiment 4, they showed old, new, and implicit objects and asked whether they recognized them or not. Object discrimination and object recognition performance did not differ between the FTPs and the controls. However, the face discrimination performance of FTP2 and face recognition performance of the FTP1 were poorer than that of the controls were. Therefore, the authors concluded that the structure of the face might affect face processing.

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.   

Why Faces May Be Special: Evidence of the Inversion Effect in Chimpanzees

1998 ◽  
Vol 10 (5) ◽  
pp. 615-622 ◽  
Author(s):  
Lisa A. Parr ◽  
Tara Dove ◽  
William D. Hopkins
Keyword(s):  
Pan Troglodytes ◽  
Homo Sapiens ◽  
Inversion Effect ◽  
Matching To Sample ◽  
Great Ape ◽  
Black And White ◽  
The Face ◽  
Face Stimuli ◽  
Human Faces ◽  
Sequential Matching

Five chimpanzees were tested on their ability to discriminate faces and automobiles presented in both their upright and inverted orientations. The face stimuli consisted of 30 black and white photographs, 10 each of unfamiliar chimpanzees (Pan troglodytes), brown capuchins (Cebus apella), and humans (Homo sapiens). Ten black and white photographs of automobiles were also used. The stimuli were presented in a sequential matching-to-sample (SMTS) format using a computerized joystick-testing apparatus. Subjects performed better on upright than inverted stimuli in all classes. Performance was significantly better on upright than inverted presentations of chimpanzee and human faces but not on capuchin monkey faces or automobiles. These data support previous studies in humans that suggest the inversion effect occurs for stimuli for which subjects have developed an expertise. Alternative explanations for the inversion effect based on the type of spatial frequency contained in the stimuli are also discussed. These data are the first to provide evidence for the inversion effect using several classes of face stimuli in a great ape species.

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 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 Feature Extraction for Face Recognition using Edge Detection and Thresholding

2020 ◽  
Vol 9 (7) ◽  
pp. 59-63
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Edge Detection ◽  
Detection System ◽  
Image Features ◽  
Face Identification ◽  
Morphological Operations ◽  
Illumination Changes ◽  
The Face ◽  
Edge Features

Face recognition is first and foremost step in video surveillance applications which include human behavioral analysis, event detection, border security and ATM banking. Most of the time, it is very difficult to get good facial features from the particular image frame and it often requires sophisticated algorithm for face identification and recognition. Robust face detection system is still a more challenging job because of complex environments including illumination changes, background clutter and occlusions. This article presents a novel feature extraction algorithm for face recognition using edge detection and thresholding. Initially, the incoming image is preprocessed to smoothen the image features and it is converted in to grayscale image to reduce the computational complexity of post processing steps. In feature extraction step, the image is completely iterated throughout the spatial coordinates and the edges are detected using thresholding technique. The optimum threshold for global thresholding is identified by calculating the maximum between-class variance in the given image. The extracted edge features are invariant under scale and illumination changes and thus it ensures the robust binary mask for face identification. Finally, the foreground features are obtained using morphological operations and the face is highlighted in subsequent incoming image frames. The proposed method can be deployed in public places such as malls, ATM centers and airports for security applications. Experimental results clearly indicate that the proposed approach works well under complex situations.

scholarly journals Attendance Management System using Face-Recognition

2021 ◽  
pp. 336-341
Author(s):  
Mrunal Aware ◽  
Prasad Labade ◽  
Manish Tambe ◽  
Aniket Jagtap ◽  
Chinmay Beldar
Keyword(s):  
Face Recognition ◽  
Management System ◽  
Educational Institutions ◽  
Face Identification ◽  
Roll Call ◽  
Student Attendance ◽  
Image Comparison ◽  
Sql Database ◽  
The Face ◽  
Computer Based

Nowadays Educational institutions are concerned about regularity of student attendance. Even in pandemic situation attendance is still a major issue in schools and colleges. Mainly there are two conventional methods of marking attendance which are calling out the roll call or by taking student sign on paper. They both were more time consuming and difficult. Hence, there is a requirement of computer-based student attendance management system which will assist the faculty for maintaining attendance record automatically. In this project we have implemented the automated attendance system using ‘TKINTER’ and ‘PYTHON’. We have projected our ideas to implement an “Automated Attendance System Based on Face Recognition”. The application includes face identification, which saves time as well as being purely softwere based it can be flagged as eco-friendly as it reduces the use of paper. This system also eliminates the chances of fake attendance because of the face being used as a biometric for authentication. Hence, this system can be implemented in a field where attendance plays an important role. The proposed system is designed in TKINTER platform supported with a script of PYTHON as well as SQL database. The algorithm used in the system is based on image comparison on the basis of the encoded values of the face from the image from database with the image recorded by the system in run time. The system has output in the form of excel sheet.

The Bandwidth of Diagnostic Horizontal Structure for Face Identification

Perception ◽  
2018 ◽  
Vol 47 (4) ◽  
pp. 397-413 ◽  
Author(s):  
Matthew V. Pachai ◽  
Patrick J. Bennett ◽  
Allison B. Sekuler
Keyword(s):  
Human Performance ◽  
Diagnostic Information ◽  
Ideal Observer ◽  
Identification Accuracy ◽  
Face Identification ◽  
Horizontal Structure ◽  
Face Inversion Effect ◽  
Wide Range ◽  
The Face ◽  
Face Stimuli

Horizontally oriented spatial frequency components are a diagnostic source of face identity information, and sensitivity to this information predicts upright identification accuracy and the magnitude of the face-inversion effect. However, the bandwidth at which this information is conveyed, and the extent to which human tuning matches this distribution of information, has yet to be characterized. We designed a 10-alternative forced choice face identification task in which upright or inverted faces were filtered to retain horizontal or vertical structure. We systematically varied the bandwidth of these filters in 10° steps and replaced the orientation components that were removed from the target face with components from the average of all possible faces. This manipulation created patterns that looked like faces but contained diagnostic information in orientation bands unknown to the observer on any given trial. Further, we quantified human performance relative to the actual information content of our face stimuli using an ideal observer with perfect knowledge of the diagnostic band. We found that the most diagnostic information for face identification is conveyed by a narrow band of orientations along the horizontal meridian, whereas human observers use information from a wide range of orientations.

scholarly journals FaceFilter: Face Identification with Deep Learning and Filter Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Mohammed Alghaili ◽  
Zhiyong Li ◽  
Hamdi A. R. Ali
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Recognition Performance ◽  
Face Identification ◽  
Original Image ◽  
Convolutional Network ◽  
The Face ◽  
In The Wild ◽  
Minimum Number ◽  
Different Levels

Although significant advances have been made recently in the field of face recognition, these have some limitations, especially when faces are in different poses or have different levels of illumination, or when the face is blurred. In this study, we present a system that can directly identify an individual under all conditions by extracting the most important features and using them to identify a person. Our method uses a deep convolutional network that is trained to extract the most important features. A filter is then used to select the most significant of these features by finding features greater than zero, storing their indices, and comparing the features of other identities with the same indices as the original image. Finally, the selected features of each identity in the dataset are subtracted from features of the original image to find the minimum number that refers to that identity. This method gives good results, as we only extract the most important features using the filter to recognize the face in different poses. We achieve state-of-the-art face recognition performance using only half of the 128 bytes per face. The system has an accuracy of 99.7% on the Labeled Faces in the Wild dataset and 94.02% on YouTube Faces DB.

Face Inversion Effect Emerges Under Critical Configural Discrepancy

2010 ◽  
Vol 69 (3) ◽  
pp. 161-167 ◽  
Author(s):  
Jisien Yang ◽  
Adrian Schwaninger
Keyword(s):  
Face Recognition ◽  
Face Processing ◽  
Underlying Mechanism ◽  
Inversion Effect ◽  
Configural Processing ◽  
Major Contributor ◽  
Face Inversion Effect ◽  
Configural Information ◽  
Face Inversion ◽  
The Face

Configural processing has been considered the major contributor to the face inversion effect (FIE) in face recognition. However, most researchers have only obtained the FIE with one specific ratio of configural alteration. It remains unclear whether the ratio of configural alteration itself can mediate the occurrence of the FIE. We aimed to clarify this issue by manipulating the configural information parametrically using six different ratios, ranging from 4% to 24%. Participants were asked to judge whether a pair of faces were entirely identical or different. The paired faces that were to be compared were presented either simultaneously (Experiment 1) or sequentially (Experiment 2). Both experiments revealed that the FIE was observed only when the ratio of configural alteration was in the intermediate range. These results indicate that even though the FIE has been frequently adopted as an index to examine the underlying mechanism of face processing, the emergence of the FIE is not robust with any configural alteration but dependent on the ratio of configural alteration.

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