The Role of Eyebrows in Face Recognition

Perception ◽  
10.1068/p5027 ◽  
2003 ◽  
Vol 32 (3) ◽  
pp. 285-293 ◽  
Author(s):  
Javid Sadr ◽  
Izzat Jarudi ◽  
Pawan Sinha
Keyword(s):  
Face Recognition ◽  
Nonverbal Communication ◽  
Emotional Expression ◽  
Recognition Performance ◽  
Facial Feature ◽  
Facial Features ◽  
Facial Aesthetics ◽  
Fundamental Challenge ◽  
Recognition Systems

A fundamental challenge in face recognition lies in determining which facial characteristics are important in the identification of faces. Several studies have indicated the significance of certain facial features in this regard, particularly internal ones such as the eyes and mouth. Surprisingly, however, one rather prominent facial feature has received little attention in this domain: the eyebrows. Past work has examined the role of eyebrows in emotional expression and nonverbal communication, as well as in facial aesthetics and sexual dimorphism. However, it has not been made clear whether the eyebrows play an important role in the identification of faces. Here, we report experimental results which suggest that for face recognition the eyebrows may be at least as influential as the eyes. Specifically, we find that the absence of eyebrows in familiar faces leads to a very large and significant disruption in recognition performance. In fact, a significantly greater decrement in face recognition is observed in the absence of eyebrows than in the absence of eyes. These results may have important implications for our understanding of the mechanisms of face recognition in humans as well as for the development of artificial face-recognition systems.

HUMAN FACE RECOGNITION FROM A SINGLE FRONT VIEW

1992 ◽  
Vol 06 (04) ◽  
pp. 571-593 ◽  
Author(s):  
CHING-WEN CHEN ◽  
CHUNG-LIN HUANG
Keyword(s):  
Face Recognition ◽  
Active Contour Model ◽  
Facial Feature ◽  
Recognition System ◽  
Facial Features ◽  
Human Face Recognition ◽  
Front View ◽  
Deformable Template ◽  
Face Recognition System ◽  
Feature Values

This paper presents a face recognition system which can identify the unknown identity effectively using the front-view facial features. In front-view facial feature extractions, we can capture the contours of eyes and mouth by the deformable template model because of their analytically describable shapes. However, the shapes of eyebrows, nostrils and face are difficult to model using a deformable template. We extract them by using the active contour model (snake). After the contours of all facial features have been captured, we calculate effective feature values from these extracted contours and construct databases for unknown identities classification. In the database generation phase, 12 models are photographed, and feature vectors are calculated for each portrait. In the identification phase if any one of these 12 persons has his picture taken again, the system can recognize his identity.

scholarly journals Data Augmentation-Assisted Makeup-Invariant Face Recognition

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Muhammad Sajid ◽  
Nouman Ali ◽  
Saadat Hanif Dar ◽  
Naeem Iqbal Ratyal ◽  
Asif Raza Butt ◽  
...  
Keyword(s):  
Face Recognition ◽  
Data Augmentation ◽  
Recognition Performance ◽  
Recognition Task ◽  
Face Images ◽  
Face Features ◽  
Face Datasets ◽  
Exponential Rates ◽  
Interpersonal Situations

Recently, face datasets containing celebrities photos with facial makeup are growing at exponential rates, making their recognition very challenging. Existing face recognition methods rely on feature extraction and reference reranking to improve the performance. However face images with facial makeup carry inherent ambiguity due to artificial colors, shading, contouring, and varying skin tones, making recognition task more difficult. The problem becomes more confound as the makeup alters the bilateral size and symmetry of the certain face components such as eyes and lips affecting the distinctiveness of faces. The ambiguity becomes even worse when different days bring different facial makeup for celebrities owing to the context of interpersonal situations and current societal makeup trends. To cope with these artificial effects, we propose to use a deep convolutional neural network (dCNN) using augmented face dataset to extract discriminative features from face images containing synthetic makeup variations. The augmented dataset containing original face images and those with synthetic make up variations allows dCNN to learn face features in a variety of facial makeup. We also evaluate the role of partial and full makeup in face images to improve the recognition performance. The experimental results on two challenging face datasets show that the proposed approach can compete with the state of the art.

Newborns' Face Recognition: Role of Inner and Outer Facial Features

2006 ◽  
Vol 77 (2) ◽  
pp. 297-311 ◽  
Author(s):  
Chiara Turati ◽  
Viola Macchi Cassia ◽  
Francesca Simion ◽  
Irene Leo
Keyword(s):  
Face Recognition ◽  
Facial Features

scholarly journals Reliability evaluation of dynamic face recognition systems based on improved Fuzzy Dynamic Bayesian Network

2020 ◽  
Vol 16 (3) ◽  
pp. 155014772091155
Author(s):  
Zhiqiang Liu ◽  
Wenbo Zhu ◽  
Hongzhou Zhang ◽  
Shengjin Wang ◽  
Lu Fang ◽  
...  
Keyword(s):  
Face Recognition ◽  
Bayesian Network ◽  
System Reliability ◽  
Evaluation Method ◽  
Recognition Performance ◽  
Dynamic Bayesian Network ◽  
Practical Significance ◽  
Identification Rate ◽  
Fuzzy C Means ◽  
Recognition Systems

The reliability of face recognition system has the characteristics of fuzziness, randomness, and continuity. In order to measure it in unconstrained scenes, we find out and quantify key broad-sense and narrow-sense influencing factors of reliability on the basis of analyzing operation states for six dynamic face recognition systems in the practical use of six public security bureaus. In this article, we propose a novel evaluation method with True Positive Identification Rate in dynamic and M:N mode and create a novel evaluation model of system reliability with the improved Fuzzy Dynamic Bayesian Network. Subsequently, we infer to solve the fuzzy reliability state probabilities of the six systems with Netica and get two most important factors with the improved fuzzy C-means algorithm. We verify the model by comparing the evaluation results with actual achievements of these systems. Finally, we find several vulnerabilities in the system with the least reliability and put forward a few optimization strategies. The proposed method combines advantages of the improved fuzzy C-means model with those of the dynamic Bayesian network to evaluate the reliability of the dynamic face recognition systems, making the evaluation results more reasonable and realistic. It starts a new research of face recognition systems in unconstrained scenes and contributes to the research on face recognition performance evaluation and system reliability analysis. Besides, the proposed method is of practical significance in improving the reliability of the systems in use.

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%.

scholarly journals The influence of face masks on emotion recognition and the role of individual differences

2021 ◽  
Author(s):  
Sarah McCrackin ◽  
Francesca Capozzi ◽  
Florence Mayrand ◽  
Jelena Ristic
Keyword(s):  
Social Competence ◽  
Personality Traits ◽  
Emotion Recognition ◽  
Recognition Performance ◽  
Facial Feature ◽  
Individual Level ◽  
Face Features ◽  
Upper Face ◽  
The Individual

With widespread adoption of mask wearing, the 2020 Covid-19 pandemic highlighted a need for a deeper understanding of how facial feature obstruction affects emotion recognition. Here we asked participants (n=120) to identify disgusted, angry, sad, neutral, surprised, happy, and fearful emotions from faces with and without masks, and examined if recognition performance was related to their level of social competence and personality traits. Performance was reduced for all masked relative to unmasked emotions. Masks impacted recognition of expressions with diagnostic lower face features the most (disgust, anger) and those with diagnostic upper face features the least (fear, surprise). Recognition performance also varied at the individual level. Persons with higher overall social competence were better at identifying unmasked expressions, while persons with lower trait extraversion and higher trait agreeableness were better at recognizing masked expressions. These results reveal novel insights about the role of face features in emotion recognition and show that obscuring facial features affects social communication differently as a function of individual social competence and personality traits.

scholarly journals Applications of PCA and SVM-PSO Based Real-Time Face Recognition System

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Ming-Yuan Shieh ◽  
Juing-Shian Chiou ◽  
Yu-Chia Hu ◽  
Kuo-Yang Wang
Keyword(s):  
Feature Selection ◽  
Face Recognition ◽  
Real Time ◽  
Selection Process ◽  
Principal Component ◽  
Facial Feature ◽  
Human Robot Interaction ◽  
Support Vector ◽  
Classification Problems ◽  
Recognition Systems

This paper incorporates principal component analysis (PCA) with support vector machine-particle swarm optimization (SVM-PSO) for developing real-time face recognition systems. The integrated scheme aims to adopt the SVM-PSO method to improve the validity of PCA based image recognition systems on dynamically visual perception. The face recognition for most human-robot interaction applications is accomplished by PCA based method because of its dimensionality reduction. However, PCA based systems are only suitable for processing the faces with the same face expressions and/or under the same view directions. Since the facial feature selection process can be considered as a problem of global combinatorial optimization in machine learning, the SVM-PSO is usually used as an optimal classifier of the system. In this paper, the PSO is used to implement a feature selection, and the SVMs serve as fitness functions of the PSO for classification problems. Experimental results demonstrate that the proposed method simplifies features effectively and obtains higher classification accuracy.

scholarly journals WGAN-E: A Generative Adversarial Networks for Facial Feature Security

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 486
Author(s):  
Chunxue Wu ◽  
Bobo Ju ◽  
Yan Wu ◽  
Neal N. Xiong ◽  
Sheng Zhang
Keyword(s):  
Face Recognition ◽  
Intelligent Systems ◽  
Human Life ◽  
Facial Feature ◽  
Generative Adversarial Networks ◽  
Facial Features ◽  
Learning Technology ◽  
Adversarial Networks ◽  
Face Features ◽  
Hashing Algorithm

Artificial intelligence technology plays an increasingly important role in human life. For example, distinguishing different people is an essential capability of many intelligent systems. To achieve this, one possible technical means is to perceive and recognize people by optical imaging of faces, so-called face recognition technology. After decades of research and development, especially the emergence of deep learning technology in recent years, face recognition has made great progress with more and more applications in the fields of security, finance, education, social security, etc. The field of computer vision has become one of the most successful branch areas. With the wide application of biometrics technology, bio-encryption technology came into being. Aiming at the problems of classical hash algorithm and face hashing algorithm based on Multiscale Block Local Binary Pattern (MB-LBP) feature improvement, this paper proposes a method based on Generative Adversarial Networks (GAN) to encrypt face features. This work uses Wasserstein Generative Adversarial Networks Encryption (WGAN-E) to encrypt facial features. Because the encryption process is an irreversible one-way process, it protects facial features well. Compared with the traditional face hashing algorithm, the experimental results show that the face feature encryption algorithm has better confidentiality.

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