scholarly journals Efficient Detection of Occlusion prior to Robust Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Rui Min ◽  
Abdenour Hadid ◽  
Jean-Luc Dugelay
Keyword(s):  
Face Recognition ◽  
Facial Expression ◽  
Video Surveillance ◽  
Real World ◽  
State Of The Art ◽  
Efficient Detection ◽  
Art Works ◽  
Enormous Amount ◽  
Recognition Systems ◽  
Robust Face Recognition

While there has been an enormous amount of research on face recognition under pose/illumination/expression changes and image degradations, problems caused by occlusions attracted relatively less attention. Facial occlusions, due, for example, to sunglasses, hat/cap, scarf, and beard, can significantly deteriorate performances of face recognition systems in uncontrolled environments such as video surveillance. The goal of this paper is to explore face recognition in the presence of partial occlusions, with emphasis on real-world scenarios (e.g., sunglasses and scarf). In this paper, we propose an efficient approach which consists of first analysing the presence of potential occlusion on a face and then conducting face recognition on the nonoccluded facial regions based on selective local Gabor binary patterns. Experiments demonstrate that the proposed method outperforms the state-of-the-art works including KLD-LGBPHS, S-LNMF, OA-LBP, and RSC. Furthermore, performances of the proposed approach are evaluated under illumination and extreme facial expression changes provide also significant results.

scholarly journals Local feature extraction based facial emotion recognition: a survey

2020 ◽  
Vol 10 (4) ◽  
pp. 4080
Author(s):  
Khadija Slimani ◽  
Mohamed Kas ◽  
Youssef El Merabet ◽  
Yassine Ruichek ◽  
Rochdi Messoussi
Keyword(s):  
Facial Expression ◽  
Large Scale ◽  
State Of The Art ◽  
Research Work ◽  
Emotional Expressions ◽  
Expression Recognition ◽  
Technological Advancement ◽  
Recognition Systems ◽  
Local Feature Extraction ◽  
Micro Patterns

Notwithstanding the recent technological advancement, the identification of facial and emotional expressions is still one of the greatest challenges scientists have ever faced. Generally, the human face is identified as a composition made up of textures arranged in micro-patterns. Currently, there has been a tremendous increase in the use of local binary pattern based texture algorithms which have invariably been identified to being essential in the completion of a variety of tasks and in the extraction of essential attributes from an image. Over the years, lots of LBP variants have been literally reviewed. However, what is left is a thorough and comprehensive analysis of their independent performance. This research work aims at filling this gap by performing a large-scale performance evaluation of 46 recent state-of-the-art LBP variants for facial expression recognition. Extensive experimental results on the well-known challenging and benchmark KDEF, JAFFE, CK and MUG databases taken under different facial expression conditions, indicate that a number of evaluated state-of-the-art LBP-like methods achieve promising results, which are better or competitive than several recent state-of-the-art facial recognition systems. Recognition rates of 100%, 98.57%, 95.92% and 100% have been reached for CK, JAFFE, KDEF and MUG databases, respectively.

Age-Invariant Face Recognition Using Triangle Geometric Features

2015 ◽  
Vol 29 (05) ◽  
pp. 1556006 ◽  
Author(s):  
Amal Seralkhatem Osman Ali ◽  
Vijanth Sagayan Asirvadam ◽  
Aamir Saeed Malik ◽  
Mohamed Meselhy Eltoukhy ◽  
Azrina Aziz
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Facial Recognition ◽  
Research Network ◽  
Facial Features ◽  
Age Difference ◽  
Facial Aging ◽  
The Face ◽  
Recognition Systems ◽  
Mathematical Relationships

Whilst facial recognition systems are vulnerable to different acquisition conditions, most notably lighting effects and pose variations, their particular level of sensitivity to facial aging effects is yet to be researched. The face recognition vendor test (FRVT) 2012's annual statement estimated deterioration in the performance of face recognition systems due to facial aging. There was about 5% degradation in the accuracies of the face recognition systems for each single year age difference between a test image and a probe image. Consequently, developing an age-invariant platform continues to be a significant requirement for building an effective facial recognition system. The main objective of this work is to address the challenge of facial aging which affects the performance of facial recognition systems. Accordingly, this work presents a geometrical model that is based on extracting a number of triangular facial features. The proposed model comprises a total of six triangular areas connecting and surrounding the main facial features (i.e. eyes, nose and mouth). Furthermore, a set of thirty mathematical relationships are developed and used for building a feature vector for each sample image. The areas and perimeters of the extracted triangular areas are calculated and used as inputs for the developed mathematical relationships. The performance of the system is evaluated over the publicly available face and gesture recognition research network (FG-NET) face aging database. The performance of the system is compared with that of some of the state-of-the-art face recognition methods and state-of-the-art age-invariant face recognition systems. Our proposed system yielded a good performance in term of classification accuracy of more than 94%.

Application of robust face recognition in video surveillance systems

2018 ◽  
Vol 14 (2) ◽  
pp. 152-155 ◽  
Author(s):  
De-xin Zhang ◽  
Peng An ◽  
Hao-xiang Zhang
Keyword(s):  
Face Recognition ◽  
Video Surveillance ◽  
Surveillance Systems ◽  
Robust Face Recognition

scholarly journals A Review on Face Anti-Spoofing

2021 ◽  
Vol 5 (1) ◽  
pp. 29
Author(s):  
Rizky Naufal Perdana ◽  
Igi Ardiyanto ◽  
Hanung Adi Nugroho
Keyword(s):  
Face Recognition ◽  
Access Control ◽  
Real World ◽  
Biometric System ◽  
Security Issue ◽  
Security Technology ◽  
The Face ◽  
Paper Review ◽  
Recognition Systems ◽  
Face Spoofing

The biometric system is a security technology that uses information based on a living person's characteristics to verify or recognize the identity, such as facial recognition. Face recognition has numerous applications in the real world, such as access control and surveillance. But face recognition has a security issue of spoofing. A face anti-spoofing, a task to prevent fake authorization by breaching the face recognition systems using a photo, video, mask, or a different substitute for an authorized person's face, is used to overcome this challenge. There is also increasing research of new datasets by providing new types of attack or diversity to reach a better generalization. This paper review of the recent development includes a general understanding of face spoofing, anti-spoofing methods, and the latest development to solve the problem against various spoof types.

scholarly journals Active AU Based Patch Weighting for Facial Expression Recognition

2017 ◽  
Author(s):  
Weicheng Xie ◽  
Linlin Shen ◽  
Meng Yang ◽  
Zhihui Lai
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
State Of The Art ◽  
Expression Recognition ◽  
Weight Optimization ◽  
Expression Variation ◽  
Feature Extraction And Selection ◽  
Art Works ◽  
Feature Optimization ◽  
New Feature

Facial expression has lots of applications in human-computer interaction. Although feature extraction and selection have been well studied, the specificity of each expression variation is not fully explored in state-of-the-art works. In this work, the problem of multiclass expression recognition is converted into triplet-wise expression recognition. For each expression triplet, a new feature optimization model based on Action Unit (AU) weighting and patch weight optimization is proposed to represent the specificity of the expression triplet. Sparse representation based approach is then proposed to detect the active AUs of testing sample for better generalization. The algorithm achieved competitive accuracies of 89.67% and 94.09% for Jaffe and CK+ databases, respectively. Better cross-database performance has also been observed.

scholarly journals Robust Face Recognition via Block Sparse Bayesian Learning

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Taiyong Li ◽  
Zhilin Zhang
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Bayesian Learning ◽  
State Of The Art ◽  
Basis Functions ◽  
Training Dataset ◽  
Sparse Bayesian Learning ◽  
Sparse Model ◽  
Test Face ◽  
Robust Face Recognition

Face recognition (FR) is an important task in pattern recognition and computer vision. Sparse representation (SR) has been demonstrated to be a powerful framework for FR. In general, an SR algorithm treats each face in a training dataset as a basis function and tries to find a sparse representation of a test face under these basis functions. The sparse representation coefficients then provide a recognition hint. Early SR algorithms are based on a basic sparse model. Recently, it has been found that algorithms based on a block sparse model can achieve better recognition rates. Based on this model, in this study, we use block sparse Bayesian learning (BSBL) to find a sparse representation of a test face for recognition. BSBL is a recently proposed framework, which has many advantages over existing block-sparse-model-based algorithms. Experimental results on the Extended Yale B, the AR, and the CMU PIE face databases show that using BSBL can achieve better recognition rates and higher robustness than state-of-the-art algorithms in most cases.

A Robust Face Representation Method for Face Recognition

2014 ◽  
Vol 945-949 ◽  
pp. 1801-1804
Author(s):  
Zhao Kui Li
Keyword(s):  
Face Recognition ◽  
Feature Vector ◽  
State Of The Art ◽  
Linear Discriminant ◽  
Face Representation ◽  
Spatial Locality ◽  
Score Method ◽  
Robust Face Recognition ◽  
Representation Method ◽  
Low Dimensional

In this paper, a robust face representation method based on multiple gradient orientations for face recognition is proposed. We introduce multiple gradient orientations and compute multiple orientation images which display different spatial locality and orientation properties. Each orientation image is normalized using the “z-score” method, and all normalized vectors are concatenated into an augmented feature vector. The dimensionality of the augmented feature vector is reduced by linear discriminant analysis to yield a low-dimensional feature vector. Experimental results show that our method achieves state-of-the-art performance for difficult problems such as illumination and occlusion-robust face recognition.

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