Diagonal Maximum Scatter Difference Analysis and its Application to Face Recognition

2010 ◽  
Vol 20-23 ◽  
pp. 123-128
Author(s):  
Jian Guo Wang ◽  
Tie Jun Zhang
Keyword(s):  
Face Recognition ◽  
Facial Features ◽  
Dimensional Vector ◽  
Human Face ◽  
Human Face Recognition ◽  
Difference Analysis ◽  
One Dimensional ◽  
Face Images ◽  
The One ◽  
Novel Algorithm

Diagonal maximum scatter difference (DiaMSD) method for face recognition is proposed in this paper. This novel algorithm is developed based on two techniques, i.e., maximum scatter difference (MSD) and diagonal face images based projection. The DiaMSD method is not only computationally more efficient but also more accurate than the one dimensional (vector-based) MSD method in extracting the facial features for human face recognition. Extensive experiments are performed to test and evaluate the new algorithm using a subset of the FERET face databases. Experimental results show the effectiveness of the proposed method (DiaMSD).

scholarly journals Multiple Data-Dependent Kernel Fisher Discriminant Analysis for Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yue Liu ◽  
Yibing Li ◽  
Hong Xie ◽  
Dandan Liu
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Facial Features ◽  
Fisher Criterion ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Face Images ◽  
Kernel Fisher Discriminant Analysis ◽  
Multiple Data ◽  
Novel Algorithm

Kernel Fisher discriminant analysis (KFDA) method has demonstrated its success in extracting facial features for face recognition. Compared to linear techniques, it can better describe the complex and nonlinear variations of face images. However, a single kernel is not always suitable for the applications of face recognition which contain data from multiple, heterogeneous sources, such as face images under huge variations of pose, illumination, and facial expression. To improve the performance of KFDA in face recognition, a novel algorithm named multiple data-dependent kernel Fisher discriminant analysis (MDKFDA) is proposed in this paper. The constructed multiple data-dependent kernel (MDK) is a combination of several base kernels with a data-dependent kernel constraint on their weights. By solving the optimization equation based on Fisher criterion and maximizing the margin criterion, the parameter optimization of data-dependent kernel and multiple base kernels is achieved. Experimental results on the three face databases validate the effectiveness of the proposed algorithm.

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.

Exploring Recognition with Interchanged Facial Features

Perception ◽  
1986 ◽  
Vol 15 (3) ◽  
pp. 235-247 ◽  
Author(s):  
Nigel D Haig
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Memory Trace ◽  
Facial Features ◽  
Relative Importance ◽  
Human Face ◽  
Human Face Recognition ◽  
Original Target ◽  
Random Responses ◽  
Dominant Influence

Any attempt to unravel the mechanism underlying the process of human face recognition must begin with experiments that explore human sensitivity to differences between a perceived image and an original memory trace. A set of three consecutive experiments are reported that were collectively designed to measure the relative importance of different facial features. The method involved the use of image-processing equipment to interchange cardinal features among frontally viewed target faces. Observers were required to indicate which of the original target faces most resembled the modified faces. The results clearly establish the dominant influence of the head outline as the major recognition feature. Next in importance is the eye/eyebrow combination, followed by the mouth, and then the nose. As a recognition feature in a frontally presented face, the nose is hardly noticed. The number of apparently random responses to some faces indicates that a surprisingly different face can sometimes arise from a fortuitous combination of the old features.

HUMAN FACE RECOGNITION FROM A SINGLE FRONT VIEW

1992 ◽  
Vol 06 (04) ◽  
pp. 571-593 ◽  
Author(s):  
CHING-WEN CHEN ◽  
CHUNG-LIN HUANG
Keyword(s):  
Face Recognition ◽  
Active Contour Model ◽  
Facial Feature ◽  
Recognition System ◽  
Facial Features ◽  
Human Face Recognition ◽  
Front View ◽  
Deformable Template ◽  
Face Recognition System ◽  
Feature Values

This paper presents a face recognition system which can identify the unknown identity effectively using the front-view facial features. In front-view facial feature extractions, we can capture the contours of eyes and mouth by the deformable template model because of their analytically describable shapes. However, the shapes of eyebrows, nostrils and face are difficult to model using a deformable template. We extract them by using the active contour model (snake). After the contours of all facial features have been captured, we calculate effective feature values from these extracted contours and construct databases for unknown identities classification. In the database generation phase, 12 models are photographed, and feature vectors are calculated for each portrait. In the identification phase if any one of these 12 persons has his picture taken again, the system can recognize his identity.

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