Auxiliary Dictionary of Diversity Learning for Face Recognition with a Single Sample Per Person

2020 ◽  
Vol 29 (05) ◽  
pp. 2050015
Author(s):  
Weifa Gan ◽  
Huixian Yang ◽  
Jinfang Zeng ◽  
Fan Chen
Keyword(s):  
Face Recognition ◽  
Objective Function ◽  
Sparse Representation ◽  
Training Sample ◽  
Single Sample ◽  
Training Set ◽  
Single Sample Per Person ◽  
Face Images ◽  
Virtual Face ◽  
Better Than

Face recognition for a single sample per person is challenging due to the lack of sufficient sample information. However, using generic training set to learn an auxiliary dictionary is an effective way to alleviate this problem. Considering generic training sample of diversity, we proposed an algorithm of auxiliary dictionary of diversity learning (ADDL). We first produced virtual face images by mirror images, square block occlusion and grey transform, and then learned an auxiliary dictionary of diversity using a designed objective function. Considering patch-based method can reduce the influence of variations, we seek extended sparse representation with l2-minimization for each probe patch. Experimental results in the CMUPIE, Extended Yale B and LFW datasets demonstrate that ADDL performs better than other related algorithms.

Producing virtual face images for single sample face recognition

Optik ◽  
2014 ◽  
Vol 125 (17) ◽  
pp. 5017-5024 ◽  
Author(s):  
Tao Zhang ◽  
Xianfeng Li ◽  
Rong-Zuo Guo
Keyword(s):  
Face Recognition ◽  
Single Sample ◽  
Face Images ◽  
Virtual Face

Nonnegative Sparse Probabilistic Estimation for Single Sample Face Recognition

2020 ◽  
Vol 34 (12) ◽  
pp. 2056008
Author(s):  
Shuhuan Zhao
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Real Life ◽  
Test Sample ◽  
Single Sample ◽  
Collaborative Representation ◽  
Adaptive Variation ◽  
Training Set ◽  
Probabilistic Estimation ◽  
Training Samples

Face recognition (FR) is a hotspot in pattern recognition and image processing for its wide applications in real life. One of the most challenging problems in FR is single sample face recognition (SSFR). In this paper, we proposed a novel algorithm based on nonnegative sparse representation, collaborative presentation, and probabilistic graph estimation to address SSFR. The proposed algorithm is named as Nonnegative Sparse Probabilistic Estimation (NNSPE). To extract the variation information from the generic training set, we first select some neighbor samples from the generic training set for each sample in the gallery set and the generic training set can be partitioned into some reference subsets. To make more meaningful reconstruction, the proposed method adopts nonnegative sparse representation to reconstruct training samples, and according to the reconstruction coefficients, NNSPE computes the probabilistic label estimation for the samples of the generic training set. Then, for a given test sample, collaborative representation (CR) is used to acquire an adaptive variation subset. Finally, the NNSPE classifies the test sample with the adaptive variation subset and probabilistic label estimation. The experiments on the AR and PIE verify the effectiveness of the proposed method both in recognition rates and time cost.

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.

Sign in / Sign up

Export Citation Format

Share Document