Repetition priming of face recognition

1987 ◽  
Vol 39 (2) ◽  
pp. 193-210 ◽  
Author(s):  
Andrew W. Ellis ◽  
Andrew W. Young ◽  
Brenda M. Flude ◽  
Dennis C. Hay
Keyword(s):  
Face Recognition ◽  
Repetition Priming ◽  
Body Shape ◽  
Prior Exposure ◽  
Partial Replication ◽  
Priming Effects ◽  
Physical Similarity ◽  
The Face

Three experiments investigating the priming of the recognition of familiar faces are reported. In Experiment 1, recognizing the face of a celebrity in an “Is this face familiar?” task was primed by exposure several minutes earlier to a different photograph of the same person, but not by exposure to the person's written name (a partial replication of Bruce and Valentine, 1985). In Experiment 2, recognizing the face of a personal acquaintance was again primed by recognizing a different photograph of their face, but not by recognizing the acquaintance from that person's body shape, clothes etc. Experiment 3 showed that maximum repetition priming is obtained from prior exposure to an identical photograph of a famous face, less from a similar photograph, and least (but still significant) from a dissimilar photograph. We argue that repetition priming is a function of the degree of physical similarity between two stimuli and that lack of priming between different stimulus types (e.g., written names and faces, or bodies and faces) may be attributable to lack of physical similarity between prime and test stimuli. Repetition priming effects may be best explained by some form of “instance-based” model such as that proposed by McClelland and Rumelhart (1985).

Incomplete Faces Don't Show the Whole Picture: Repetition Priming from Jumbled Faces

1996 ◽  
Vol 49 (3) ◽  
pp. 596-615 ◽  
Author(s):  
Robert A. Johnston ◽  
Christopher Barry ◽  
Catherine Williams
Keyword(s):  
Repetition Priming ◽  
Response Times ◽  
Target Face ◽  
Prior Exposure ◽  
Famous People ◽  
Training Stage ◽  
The Face ◽  
The Subject

Four experiments examined repetition priming of familiarity decisions to faces of famous people by the prior exposure of intact or jumbled faces. In Experiments 1 and 2 the primes were either the identical picture of the target face or a picture of the face with the internal features jumbled up. (In Experiment 2 the external features were also removed from all faces.) Compared with response times to previously unseen faces, familiarity decisions were made more rapidly if the subject had seen and identified the famous face in the pre-training stage; this was independent of whether they saw an intact or jumbled face. Priming was not shown if the face was not recognized earlier. Experiment 3 demonstrated that, if faces were not recognized spontaneously in the pre-training stage, being prompted as to their identity by the experimenter still did not yield priming at test—a result that replicated a previous study using incomplete faces (Brunas-Wagstaff, Young, & Ellis, 1992). Experiment 4 showed that it was the situation in which the information was given that was critical in determining whether priming occurred. The findings of this study are related to mechanisms for repetition priming of faces and used to discuss the necessity of modifications to the Bruce and Young (1986) model such as those offered by Burton, Bruce, and Johnston (1990).

Cognitive mechanisms of face processing

1992 ◽  
Vol 335 (1273) ◽  
pp. 113-119 ◽  
Keyword(s):  
Face Recognition ◽  
Repetition Priming ◽  
Semantic Knowledge ◽  
Recent Past ◽  
Cognitive Mechanisms ◽  
Cognitive Representations ◽  
Familiar Face ◽  
The Face ◽  
Related Individuals ◽  
Familiar Person

Evidence from natural and induced errors of face recognition, from the effects of different cues on resolving errors, and from the latencies to make different decisions about seen faces, all suggest that familiar face recognition involves a fixed, invariant sequence of stages. To recognize a familiar face, a perceptual description of a seen face must first activate a long-standing representation of the appearance of the face of the familiar person. ‘Semantic’ knowledge about such things as the person’s occupation and personality are accessed next, followed, in the final stage, by the name. Certain factors affect the ease of familiar face recognition. Faces seen in the recent past are recognized more readily (repetition priming), as are distinctive faces, and faces preceded by those of related individuals (associative priming). Our knowledge of these phenomena is reviewed for the light it can shed upon the mechanisms of face recognition. Four aspects of face recognition - graded similarity effects and part-to-whole completion in repetition priming, prototype extraction with simultaneous retention of information about individual exemplars, and distinctiveness effects in classification and identification - are proposed as being compatible with distributed memory accounts of cognitive representations.

Effects of Facial Identity on Age Judgments

2010 ◽  
Vol 57 (5) ◽  
pp. 390-397 ◽  
Author(s):  
Yuly Dagovitch ◽  
Tzvi Ganel
Keyword(s):  
Face Recognition ◽  
Repetition Priming ◽  
Experimental Session ◽  
Priming Effects ◽  
Facial Identity ◽  
Priming Paradigm ◽  
Memory Representations ◽  
The Relationship ◽  
Unfamiliar Faces ◽  
Facial Age

According to current face recognition models, facial identity is processed independently from other visually derived facial aspects, such as facial age. Here we used a repetition priming paradigm to investigate the relationship between the processing of facial identity and facial age. In Experiment 1, participants made speeded age classifications for primed and unprimed faces of famous celebrities. Performance was faster and more accurate for primed compared to unprimed faces, which indicates that the processing of facial age benefits from priming effects. In Experiment 2, priming was also found for preexperimentally unfamiliar faces which were familiarized during the experimental session. In Experiment 3, priming effects were found even when different photos of the same people were presented at study and at test. These results suggest that the processing of age is mediated by memory representations of facial identity.

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.

Real Time Person Recognition and Attendance Marking Using Holistic Based Approach

2017 ◽  
Vol 7 (7) ◽  
pp. 302
Author(s):  
Reshma P ◽  
Muneer VK ◽  
Muhammed Ilyas P
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
High Accuracy ◽  
Efficient Technique ◽  
Human Face ◽  
Traditional Methods ◽  
Person Recognition ◽  
The Face

Face recognition is a challenging task for the researches. It is very useful for personal verification and recognition and also it is very difficult to implement due to all different situation that a human face can be found. This system makes use of the face recognition approach for the computerized attendance marking of students or employees in the room environment without lectures intervention or the employee. This system is very efficient and requires very less maintenance compared to the traditional methods. Among existing methods PCA is the most efficient technique. In this project Holistic based approach is adapted. The system is implemented using MATLAB and provides high accuracy.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

Face recognition in an intelligent door lock with ResNet model based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Suphawimon Phawinee ◽  
Jing-Fang Cai ◽  
Zhe-Yu Guo ◽  
Hao-Ze Zheng ◽  
Guan-Chen Chen
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Low Cost ◽  
Ease Of Use ◽  
Raspberry Pi ◽  
Training Methods ◽  
Graphics Recognition ◽  
Face Images ◽  
The Face ◽  
Door Lock

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

scholarly journals View-tuned and view-invariant face encoding in IT cortex is explained by selected natural image fragments

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yunjun Nam ◽  
Takayuki Sato ◽  
Go Uchida ◽  
Ekaterina Malakhova ◽  
Shimon Ullman ◽  
...  
Keyword(s):  
Face Recognition ◽  
Visual Pathway ◽  
Visual Features ◽  
Natural Image ◽  
Face Representation ◽  
Low Level ◽  
Ventral Visual Pathway ◽  
Neural Substrate ◽  
The Face

AbstractHumans recognize individual faces regardless of variation in the facial view. The view-tuned face neurons in the inferior temporal (IT) cortex are regarded as the neural substrate for view-invariant face recognition. This study approximated visual features encoded by these neurons as combinations of local orientations and colors, originated from natural image fragments. The resultant features reproduced the preference of these neurons to particular facial views. We also found that faces of one identity were separable from the faces of other identities in a space where each axis represented one of these features. These results suggested that view-invariant face representation was established by combining view sensitive visual features. The face representation with these features suggested that, with respect to view-invariant face representation, the seemingly complex and deeply layered ventral visual pathway can be approximated via a shallow network, comprised of layers of low-level processing for local orientations and colors (V1/V2-level) and the layers which detect particular sets of low-level elements derived from natural image fragments (IT-level).

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 Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Naeem Ratyal ◽  
Imtiaz Ahmad Taj ◽  
Muhammad Sajid ◽  
Anzar Mahmood ◽  
Sohail Razzaq ◽  
...  
Keyword(s):  
Face Recognition ◽  
Superior Performance ◽  
Support Vector ◽  
Complex Nature ◽  
Alignment Algorithm ◽  
3D Face Recognition ◽  
3D Face ◽  
Norm Minimization ◽  
Wide Range ◽  
The Face

Face recognition aims to establish the identity of a person based on facial characteristics and is a challenging problem due to complex nature of the facial manifold. A wide range of face recognition applications are based on classification techniques and a class label is assigned to the test image that belongs to the unknown class. In this paper, a pose invariant deeply learned multiview 3D face recognition approach is proposed and aims to address two problems: face alignment and face recognition through identification and verification setups. The proposed alignment algorithm is capable of handling frontal as well as profile face images. It employs a nose tip heuristic based pose learning approach to estimate acquisition pose of the face followed by coarse to fine nose tip alignment using L2 norm minimization. The whole face is then aligned through transformation using knowledge learned from nose tip alignment. Inspired by the intrinsic facial symmetry of the Left Half Face (LHF) and Right Half Face (RHF), Deeply learned (d) Multi-View Average Half Face (d-MVAHF) features are employed for face identification using deep convolutional neural network (dCNN). For face verification d-MVAHF-Support Vector Machine (d-MVAHF-SVM) approach is employed. The performance of the proposed methodology is demonstrated through extensive experiments performed on four databases: GavabDB, Bosphorus, UMB-DB, and FRGC v2.0. The results show that the proposed approach yields superior performance as compared to existing state-of-the-art methods.

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