Manipulating Face Gender

1998 ◽  
Vol 06 (03) ◽  
pp. 219-239 ◽  
Author(s):  
Kenneth A. Deffenbacher ◽  
Cheryl Hendrickson ◽  
Alice J. O'Toole ◽  
David P. Huff ◽  
Hervé Abdi
Keyword(s):  
Face Recognition ◽  
Computational Model ◽  
Face Image ◽  
Gender Classification ◽  
Statistical Features ◽  
Female Adult ◽  
Recognition Experiment ◽  
Accuracy Data ◽  
Speed And Accuracy ◽  
Weighted Combination

Previous research has shown that faces coded as pixel-based images may be constructed from an appropriately weighted combination of statistical "features" (eigenvectors) which are useful for discriminating members of a learned set of images. We have shown previously that two of the most heavily weighted features are important in predicting face gender. Using a simple computational model, we adjusted weightings of these features in more masculine and more feminine directions for both male and female adult Caucasian faces. In Experiment 1, cross-gender face image alterations (e.g., feminizing male faces) reduced both gender classification speed and accuracy for young adult Caucasian observers, whereas same-gender alterations (e.g., masculinizing male faces) had no effect as compared to unaltered controls. Effects on femininity-masculinity ratings mirrored those obtained on gender classification speed and accuracy. We controlled statistically for possible effects of image distortion incurred by our gender manipulations. In Experiment 2 we replicated the same pattern of accuracy data. Combined, these data indicate the psychological relevance of the features derived from the computational model. Despite having different effects on the ease of gender classification, neither sort of gender alteration negatively impacted face recognition (Experiment 3), yielding evidence for a model of face recognition wherein gender and familiarity processing proceed in parallel.

The Research of Face Recognition Method Based on Wavelet Transform

2012 ◽  
Vol 442 ◽  
pp. 463-467
Author(s):  
Li Hong Bi ◽  
Yan Fang Ma ◽  
Li Hua Piao
Keyword(s):  
Face Recognition ◽  
Wavelet Transform ◽  
Principal Component ◽  
Face Image ◽  
Biometric Identification ◽  
Recognition Method ◽  
Image Block ◽  
The Face ◽  
Speed And Accuracy ◽  
Great Development

Face recognition is a kind of biometric identification technology possessing great development potential, researching on this technology has great theoretical values. The paper presents a method of image block principal component analysis (PCA) based on wavelet transform. The image was firstly disposed by wavelet transform and segmented, then we set the different weight values for each of parts according to the different role in the overall image and extract eigenvector using the PCA, finally, the face image is recognized according to the eigenvector and feature. This method can improve the speed and accuracy, reduce the complexity of feature extraction and improve the speed of recognition.

Adapted discriminative coupled mappings for low-resolution face recognition with one-shot

2021 ◽  
pp. 1-15
Author(s):  
Yongjie Chu ◽  
Touqeer Ahmad ◽  
Lindu Zhao
Keyword(s):  
Face Recognition ◽  
Law Enforcement ◽  
High Resolution ◽  
Domain Adaptation ◽  
Face Image ◽  
Discriminative Learning ◽  
Low Resolution ◽  
Face Images ◽  
World Environment

Low-resolution face recognition with one-shot is a prevalent problem encountered in law enforcement, where it generally requires to recognize the low-resolution face images captured by surveillance cameras with the only one high-resolution profile face image in the database. The problem is very tough because the available samples is quite few and the quality of unknown images is quite low. To effectively address this issue, this paper proposes Adapted Discriminative Coupled Mappings (AdaDCM) approach, which integrates domain adaptation and discriminative learning. To achieve good domain adaptation performance for small size dataset, a new domain adaptation technique called Bidirectional Locality Matching-based Domain Adaptation (BLM-DA) is first developed. Then the proposed AdaDCM is formulated by unifying BLM-DA and discriminative coupled mappings into a single framework. AdaDCM is extensively evaluated on FERET, LFW, and SCface databases, which includes LR face images obtained in constrained, unconstrained, and real-world environment. The promising results on these datasets demonstrate the effectiveness of AdaDCM in LR face recognition with one-shot.

Low-Resolution Face Recognition with Single Sample per Person via Domain Adaptation

2019 ◽  
Vol 33 (05) ◽  
pp. 1956005 ◽  
Author(s):  
Yongjie Chu ◽  
Yong Zhao ◽  
Touqeer Ahmad ◽  
Lindu Zhao
Keyword(s):  
Face Recognition ◽  
Domain Adaptation ◽  
Face Image ◽  
Single Sample ◽  
Low Resolution ◽  
Target Domain ◽  
Discriminative Ability ◽  
Surveillance Camera ◽  
Single Sample Per Person ◽  
Suspect Identification

Numerous low-resolution (LR) face images are captured by a growing number of surveillance cameras nowadays. In some particular applications, such as suspect identification, it is required to recognize an LR face image captured by the surveillance camera using only one high-resolution (HR) profile face image on the ID card. This leads to LR face recognition with single sample per person (SSPP), which is more challenging than conventional LR face recognition or SSPP face recognition. To address this tough problem, we propose a Boosted Coupled Marginal Fisher Analysis (CMFA) approach, which unites domain adaptation and coupled mappings. An auxiliary database containing multiple HR and LR samples is introduced to explore more discriminative information, and locality preserving domain adaption (LPDA) is designed to realize good domain adaptation between SSPP training set (target domain) and auxiliary database (source domain). We perform LPDA on HR and LR images in both domains, then in the domain adaptation space we apply CMFA to learn the discriminative coupled mappings for classification. The learned coupled mappings embed knowledge from the auxiliary dataset, thus their discriminative ability is superior. We extensively evaluate the proposed method on FERET, LFW and SCface database, the promising results demonstrate its effectiveness on LR face recognition with SSPP.

scholarly journals Cyborg Groups Enhance Face Recognition in Crowded Environments

2018 ◽  
Author(s):  
Davide Valeriani ◽  
Riccardo Poli
Keyword(s):  
Decision Making ◽  
Face Recognition ◽  
Machine Vision ◽  
Decision Confidence ◽  
Computer Interfaces ◽  
Recognition Experiment ◽  
Or Groups ◽  
Individual Decisions ◽  
Human Participants ◽  
Crowded Environments

AbstractRecognizing a person in a crowded environment is a challenging, yet critical, visual-search task for both humans and machine-vision algorithms. This paper explores the possibility of combining a residual neural network (ResNet), brain-computer interfaces (BCIs) and human participants to create “cyborgs” that improve decision making. Human participants and a ResNet undertook the same face-recognition experiment. BCIs were used to decode the decision confidence of humans from their EEG signals. Different types of cyborg groups were created, including either only humans (with or without the BCI) or groups of humans and the ResNet. Cyborg groups decisions were obtained weighing individual decisions by confidence estimates. Results show that groups of cyborgs are significantly more accurate (up to 35%) than the ResNet, the average participant, and equally-sized groups of humans not assisted by technology. These results suggest that melding humans, BCI, and machine-vision technology could significantly improve decision-making in realistic scenarios.

Assessment of Graphics and Text Formats for System Status Displays

1992 ◽  
Vol 36 (18) ◽  
pp. 1464-1468 ◽  
Author(s):  
William A. Nugent ◽  
James W. Broyles
Keyword(s):  
Relative Effectiveness ◽  
Response Speed ◽  
Practical Applications ◽  
Reliable Performance ◽  
Accurate Performance ◽  
Presentation Formats ◽  
Computer Based ◽  
Status Data ◽  
Accuracy Data ◽  
Speed And Accuracy

This study compared the relative effectiveness of three computer-based formats for displaying Navy system status data. Response speed and accuracy data were collected for each format on four tasks typically performed in a shipboard Combat Information Center (CIC). The three presentation formats were character readout (CRO), text-only, and text-graphics. Results showed the text-only and text-graphics formats produced faster, more accurate performance than the CRO on count and compare tasks; however, no reliable performance differences were found between presentation formats for identify and criterion tasks. Predictions concerning an advantage for the text-graphics format over the text-only format on certain types of tasks were not supported by the study findings. The practical applications and design implications of these findings are discussed.

Automatic Face Image Tagging in Large Collections

2014 ◽  
pp. 336-358
Author(s):  
Silvio Barra ◽  
Maria De Marsico ◽  
Chiara Galdi
Keyword(s):  
Social Networks ◽  
Face Recognition ◽  
Large Scale ◽  
Face Image ◽  
Image Tagging ◽  
Indexing And Retrieval ◽  
Magazine Publishing ◽  
Face Datasets ◽  
Traditional Approaches ◽  
Personal Devices

In this chapter, the authors present some issues related to automatic face image tagging techniques. Their main purpose in user applications is to support the organization (indexing) and retrieval (or easy browsing) of images or videos in large collections. Their core modules include algorithms and strategies for handling very large face databases, mostly acquired in real conditions. As a background for understanding how automatic face tagging works, an overview about face recognition techniques is given, including both traditional approaches and novel proposed techniques for face recognition in uncontrolled settings. Moreover, some applications and the way they work are summarized, in order to depict the state of the art in this area of face recognition research. Actually, many of them are used to tag faces and to organize photo albums with respect to the person(s) presented in annotated photos. This kind of activity has recently expanded from personal devices to social networks, and can also significantly support more demanding tasks, such as automatic handling of large editorial collections for magazine publishing and archiving. Finally, a number of approaches to large-scale face datasets as well as some automatic face image tagging techniques are presented and compared. The authors show that many approaches, both in commercial and research applications, still provide only a semi-automatic solution for this problem.

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