Deep Cascade Model-Based Face Recognition: When Deep-Layered Learning Meets Small Data

2020 ◽  
Vol 29 ◽  
pp. 1016-1029 ◽  
Author(s):  
Lei Zhang ◽  
Ji Liu ◽  
Bob Zhang ◽  
David Zhang ◽  
Ce Zhu
Keyword(s):  
Face Recognition ◽  
Cascade Model ◽  
Small Data ◽  
Model Based ◽  
Layered Learning

scholarly journals DeepWTPCA-L1: A New Deep Face Recognition Model Based on WTPCA-L1 Norm Features

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 65091-65100
Author(s):  
Ayyad Maafiri ◽  
Omar Elharrouss ◽  
Saad Rfifi ◽  
Somaya Ali Al-Maadeed ◽  
Khalid Chougdali
Keyword(s):  
Face Recognition ◽  
L1 Norm ◽  
Recognition Model ◽  
Model Based

3D model-based pose invariant face recognition from multiple views

2007 ◽  
Vol 1 (1) ◽  
pp. 25-34 ◽  
Author(s):  
Q. Chen ◽  
J. Yao ◽  
W.K. Cham
Keyword(s):  
Face Recognition ◽  
3D Model ◽  
Multiple Views ◽  
Model Based

scholarly journals Characterizing developmental prosopagnosia beyond face perception: Impaired recollection but intact familiarity recognition

2020 ◽  
Author(s):  
Anna Stumps ◽  
Elyana Saad ◽  
David Rothlein ◽  
Mieke Verfaellie ◽  
Joseph DeGutis
Keyword(s):  
Face Recognition ◽  
Face Perception ◽  
Roc Analysis ◽  
Recognition Task ◽  
High Confidence ◽  
Perceptual Deficit ◽  
Model Based ◽  
Confidence Ratings

Converging lines of research suggests that many developmental prosopagnosics (DPs) have impairments beyond face perception, but currently no framework exists to characterize these impaired mechanisms. One potential extra-perceptual deficit is that DPs encode/retrieve faces in a distinct manner from controls that does not sufficiently support individuation. To test this possibility, 30 DPs and 30 matched controls performed an old/new face recognition task while providing confidence ratings, to which a model-based ROC analysis was applied. DPs had significantly reduced recollection compared to controls, driven by fewer ‘high-confidence target’ responses, but intact familiarity. Recollection and face perception ability uniquely predicted objective and subjective prosopagnosia symptoms, together explaining 51% and 56% of the variance, respectively. These results suggest that a specific deficit in face recollection in DP may represent a core aspect of the difficulty in confidently identifying an individual by their face.

scholarly journals On-the-fly Ambiguity Resolution Using an Estimator of the Modified Ambiguity Covariance Matrix for the GNSS Positioning Model Based on Phase Data

2012 ◽  
Vol 47 (3) ◽  
pp. 81-90 ◽  
Author(s):  
S. Cellmer
Keyword(s):  
Covariance Matrix ◽  
Ambiguity Resolution ◽  
Positive Definiteness ◽  
Small Data ◽  
Data Set ◽  
Gnss Positioning ◽  
Model Based ◽  
Lambda Method ◽  
Phase Data ◽  
Single Epoch

On-the-fly Ambiguity Resolution Using an Estimator of the Modified Ambiguity Covariance Matrix for the GNSS Positioning Model Based on Phase Data On-the-fly ambiguity resolution (OTF AR) is based on a small data set, obtained from a very short observation session or even from a single epoch observation. In these cases, a classical approach to ambiguity resolution (e.g. the Lambda method) can meet some numerical problems. The basis of the Lambda method is an integer decorrelation of the positive definite ambiguity covariance matrix (ACM). The necessary condition for the proper performing of this procedure is a positive definiteness of ACM. However, this condition is not satisfied in cases of very short observation sessions or single epoch positioning if phase-only observations are used. The subject of this contribution is such a case where phase-only observations are used in the final part of the computational process. The modification of ACM is proposed in order to ensure its positive definiteness. An estimator of modified ACM is a good ACM approximation for the purpose of performing the LAMBDA method. Another problem of short sessions (or a single epoch) positioning is the poor quality of the float solution. In this paper, a cascade adjustment with wide-lane combinations of signals L1 and L2 as a method of solving this problem is presented.

scholarly journals Robust Face Recognition Using the Deep C2D-CNN Model Based on Decision-Level Fusion

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2080 ◽  
Author(s):  
Jing Li ◽  
Tao Qiu ◽  
Chang Wen ◽  
Kai Xie ◽  
Fang-Qing Wen
Keyword(s):  
Face Recognition ◽  
Decision Level ◽  
Model Based ◽  
Decision Level Fusion ◽  
Robust Face Recognition ◽  
Level Fusion

scholarly journals A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Mohammed Hasan Abdulameer ◽  
Siti Norul Huda Sheikh Abdullah ◽  
Zulaiha Ali Othman
Keyword(s):  
Face Recognition ◽  
Model Fitting ◽  
Optimization Method ◽  
Appearance Model ◽  
Active Appearance Model ◽  
Abc Algorithm ◽  
Model Based ◽  
Bee Colony ◽  
Active Appearance ◽  
Recognition Technique

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.

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