Research on face recognition technology and its application in intelligent security

2019 ◽  
Author(s):  
Haibo Chen ◽  
Lei Liu ◽  
Jing Tang
Keyword(s):  
Face Recognition ◽  
Intelligent Security

scholarly journals Smart Gate: Intelligent Security System Based on Face Recognition

2020 ◽  
Vol 9 (3) ◽  
pp. 601-608
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Identity Verification ◽  
Lighting Condition ◽  
Face Recognition System ◽  
The Face ◽  
Degree Of Orientation ◽  
Gate System ◽  
Intelligent Security ◽  
And Storage

Features of Human face is one of the unique biometrics used for identifying and recognizing humans. This makes face recognition system an integral component of numerous applications like identity verification of personnel at gates in many organizations, for controlled access of confidential resources, recognizing intruders by nationwide defense institutions, and such many more. Due to this the unavoidable need of face recognition system, practitioners and researchers are putting their continuous efforts to rectify and optimize the face recognition system for different perspectives in terms of accuracy, time, and storage. The techniques are also optimized for face captured at different degree of orientation, change of facial expression with time, lighting condition, with and without eyeglass and other props, but as isolated solutions. The main aim of the paper is to propose a system that can be installed at any door or gate which would operate based on integrated face recognition. The major contribution of this paper is twofold: i) An integrated face recognition solution optimized for recognizing multiple people faces captured at different orientations and even wearing eyeglasses of different shapes, sizes and materials ii) Smart Gate system being activated upon successful recognition of all faces captured simultaneously.

scholarly journals A New Face Recognition Method for Intelligent Security

2020 ◽  
Vol 10 (3) ◽  
pp. 852 ◽  
Author(s):  
Zhenzhou Wang ◽  
Xu Zhang ◽  
Pingping Yu ◽  
Wenjie Duan ◽  
Dongjie Zhu ◽  
...  
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Recognition Method ◽  
3D Face Reconstruction ◽  
Recognition Time ◽  
Security Industry ◽  
Face Reconstruction ◽  
Weighted Fusion ◽  
Intelligent Security

With the advent of the era of artificial intelligence and big data, intelligent security robots not only improve the efficiency of the traditional intelligent security industry but also propose higher requirements for intelligent security. Aiming to solve the problem of long recognition time and high equipment cost of intelligent security robots, we propose a new face recognition method for intelligent security in this paper. We use the Goldstein branching method for phase unwrapping, which can improve the three-dimensional (3D) face reconstruction effect. Subsequently, by using the three-dimensional face recognition method based on face radial curve elastic matching, different weights are assigned to different curve recognition similarity for weighted fusion as the total similarity for recognition. Experiments show that the method has a higher face recognition rate and is robust to attitude, illumination, and noise.

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.

Perceptual Effects and Recollective Experience in Face Recognition

2005 ◽  
Vol 52 (3) ◽  
pp. 224-231 ◽  
Author(s):  
Chrisanthi Nega
Keyword(s):  
Face Recognition ◽  
Recognition Memory ◽  
Congruency Effect ◽  
Facial Recognition ◽  
Memory Systems ◽  
Study Time ◽  
Perceptual Fluency ◽  
Recollective Experience ◽  
Trace Strength ◽  
Size Congruency

Abstract. Four experiments were conducted investigating the effect of size congruency on facial recognition memory, measured by remember, know and guess responses. Different study times were employed, that is extremely short (300 and 700 ms), short (1,000 ms), and long times (5,000 ms). With the short study time (1,000 ms) size congruency occurred in knowing. With the long study time the effect of size congruency occurred in remembering. These results support the distinctiveness/fluency account of remembering and knowing as well as the memory systems account, since the size congruency effect that occurred in knowing under conditions that facilitated perceptual fluency also occurred independently in remembering under conditions that facilitated elaborative encoding. They do not support the idea that remember and know responses reflect differences in trace strength.

Proclivity to Choose on Face Recognition Predicts PTC on Lineups

2014 ◽  
Author(s):  
Mario Baldassari ◽  
Justin Kantner ◽  
D. Stephen Lindsay
Keyword(s):  
Face Recognition

Emotion and Own and Other-Race Face Recognition

2010 ◽  
Author(s):  
Margaret Tsai ◽  
Jennifer Groscup
Keyword(s):  
Face Recognition
Sign in / Sign up

Export Citation Format

Share Document