Combining DCT and LBP Feature Sets For Efficient Face Recognition

2008 ◽  
Author(s):  
Mohamed El Aroussi ◽  
Aouatif Amine ◽  
Sanaa Ghouzali ◽  
Mohammed Rziza ◽  
Driss Aboutajdine
Keyword(s):  
Face Recognition ◽  
Feature Sets

Illumination, Pose and Occlusion Invariant Face Recognition from Range Images Using ERFI Model

2015 ◽  
Vol 4 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Suranjan Ganguly ◽  
Debotosh Bhattacharjee ◽  
Mita Nasipuri
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Range Images ◽  
Sequence Matching ◽  
Subjective Analysis ◽  
3D Face ◽  
Feature Sets ◽  
Face Images ◽  
Weighted Fusion ◽  
Matching Technique

In this paper the pivotal contribution of the authors is to recognize the 3D face images from range images in the unconstrained environment i.e. under varying illumination, pose as well as occlusion that are considered to be the most challenging task in the domain of face recognition. During this investigation, face images have been normalized in terms of pose registration as well as occlusion restoration using ERFI (Energy Range Face Image) model. 3D face images are inherently illumination invariant due its point-based representation of data along three axes. Here, other than quantitative analysis, a subjective analysis is also carried out. However, synthesized datasets have been accomplished to investigate the performance of recognition rate from Frav3D and Bosphorus databases using SIFT and SURF like features. Moreover, weighted fusion of these individual feature sets is also done. Later these feature sets have been classified by K-NN and Sequence Matching Technique and achieved maximum recognition rates of 99.17% and 98.81% for Frav3D and GavabDB databases respectively.

scholarly journals Complementary feature sets for optimal face recognition

2014 ◽  
Vol 2014 (1) ◽  
Author(s):  
Chandan Singh ◽  
Neerja Mittal ◽  
Ekta Walia
Keyword(s):  
Face Recognition ◽  
Feature Sets

scholarly journals Partially Occluded Face Recognition using Dynamic Time Wrapping

2018 ◽  
Vol 7 (2.22) ◽  
pp. 31 ◽  
Author(s):  
Ashish Kumar ◽  
P Shanmugavadivu
Keyword(s):  
Face Recognition ◽  
Phase I ◽  
Feature Sets ◽  
Face Images ◽  
Average Accuracy ◽  
The Face ◽  
Two Phases ◽  
Dynamic Time ◽  
Partially Occluded ◽  
Dynamic Time Wrapping

It is evident that the research contributions in the domain of partially occluded image are quite sparse. This paper presents a novel method, termed as Partially Occluded Face Recognition (POFR) using Maximally Stable External Regions (MSER) feature sets and Dynamic Time Wrapping (DTW). This proposed system works in two phases: Phase-I, creates an annotated database using the non-occluded images, and Phase-II focuses on the detection and recognition of partially occluded probe image, which is also annotated using the mechanism of phase-I. Hence, POFR selectively and dynamically calibrates the annotated database as per the annotation of the probe image. Further, the similarity between the feature sets of the annotated database images and the probe image is computed, using the principle of DTW. The POFR is tested on the face images from University of Stirling dataset and the average accuracy of face recognition is recorded as 88%. This method promises a computational advantage for partially occluded face recognition without any prior reconstruction or synthesis. The POFR finds direct applications in surveillance and security systems.  

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.

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