Contribution of Color to Face Recognition

Perception ◽  
10.1068/p3376 ◽  
2002 ◽  
Vol 31 (8) ◽  
pp. 995-1003 ◽  
Author(s):  
Andrew W Yip ◽  
Pawan Sinha
Keyword(s):  
Image Analysis ◽  
Face Recognition ◽  
Recognition Performance ◽  
Past Research ◽  
Color Images ◽  
Experimental Results ◽  
Facial Attributes ◽  
Color Cues ◽  
Better Than

One of the key challenges in face perception lies in determining how different facial attributes contribute to judgments of identity. In this study, we focus on the role of color cues. Although color appears to be a salient attribute of faces, past research has suggested that it confers little recognition advantage for identifying people. Here we report experimental results suggesting that color cues do play a role in face recognition and their contribution becomes evident when shape cues are degraded. Under such conditions, recognition performance with color images is significantly better than that with gray-scale images. Our experimental results also indicate that the contribution of color may lie not so much in providing diagnostic cues to identity as in aiding low-level image-analysis processes such as segmentation.

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.

scholarly journals Things are Looking up: Differential Decline in Face Recognition following Pitch and Yaw Rotation

Perception ◽  
10.1068/p5637 ◽  
2007 ◽  
Vol 36 (9) ◽  
pp. 1334-1352 ◽  
Author(s):  
Simone K Favelle ◽  
Stephen Palmisano ◽  
Ryan T Maloney
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Recognition Task ◽  
Vertical Axis ◽  
Matching Task ◽  
Human Face ◽  
Human Face Recognition ◽  
Viewpoint Change ◽  
Sequential Matching ◽  
Better Than

Previous research into the effects of viewpoint change on face recognition has typically dealt with rotations around the head's vertical axis (yaw). Another common, although less studied, source of viewpoint variation in faces is rotation around the head's horizontal pitch axis (pitch). In the current study we used both a sequential matching task and an old/new recognition task to examine the effect of viewpoint change following rotation about both pitch and yaw axes on human face recognition. The results of both tasks showed that recognition performance was better for faces rotated about yaw compared to pitch. Further, recognition performance for faces rotated upwards on the pitch axis was better than for faces rotated downwards. Thus, equivalent angular rotations about pitch and yaw do not produce equivalent viewpoint-dependent declines in recognition performance.

AN EVALUATION ON PERFORMANCE OF PCA IN FACE RECOGNITION WITH EXPRESSION VARIATIONS

2018 ◽  
Vol 1 (30) ◽  
pp. 61-66
Author(s):  
Khanh Ngoc Van Duong ◽  
An Bao Nguyen
Keyword(s):  
Face Recognition ◽  
Facial Expression ◽  
Mouth Opening ◽  
Experimental Results ◽  
Classification Algorithms ◽  
Test Cases ◽  
Wide Mouth ◽  
Better Than

Appearance-based recognition methods often encounter difficulties when the input images contain facial expression variations such as laughing, crying or wide mouth opening. In these cases, holistic methods give better performance than appearance-based methods. This paper presents some evaluation on face recognition under variation of facial expression  using the combination of PCA and classification algorithms. The experimental results showed that the best accuracy can be obtained with very few eigenvectors and KNN algorithm (with k=1) performs better than SVM in most test cases.

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.

scholarly journals Real-World Face Recognition: The Importance of Surface Reflectance Properties

Perception ◽  
10.1068/p5779 ◽  
2007 ◽  
Vol 36 (9) ◽  
pp. 1368-1374 ◽  
Author(s):  
Richard Russell ◽  
Pawan Sinha
Keyword(s):  
Face Recognition ◽  
Real World ◽  
Recognition Task ◽  
Color Images ◽  
Surface Reflectance ◽  
Shape Information ◽  
Familiar Face ◽  
The Face ◽  
Unfamiliar Face ◽  
Facial Attributes

The face recognition task we perform most often in everyday experience is the identification of people with whom we are familiar. However, because of logistical challenges, most studies focus on unfamiliar-face recognition, wherein subjects are asked to match or remember images of unfamiliar people's faces. Here we explore the importance of two facial attributes—shape and surface reflectance—in the context of a familiar-face recognition task. In our experiment, subjects were asked to recognise color images of the faces of their friends. The images were manipulated such that only reflectance or only shape information was useful for recognizing any particular face. Subjects were actually better at recognizing their friends' faces from reflectance information than from shape information. This provides evidence that reflectance information is important for face recognition in ecologically relevant contexts.

An Improved Block 2DPCA Face Recognition Algorithm with L1-Norm

2012 ◽  
Vol 457-458 ◽  
pp. 1077-1082 ◽  
Author(s):  
Hao Zheng ◽  
Lei Pan
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Recognition Algorithm ◽  
Experimental Results ◽  
Frobenius Norm ◽  
Face Database ◽  
L1 Norm

Most of the existing Block 2DPCA algorithms are based on Frobenius norm, which makes them sensitive to outliers. In this paper, we propose a new improved Block 2DPCA algorithm with L1-norm, which is robust to outliers. The experimental results of FERET face database indicated that the recognition performance of new algorithm is superior to Block 2DPCA, more robust than Block 2DPCA.

scholarly journals Face Recognition Using Complex Valued Backpropagation

2018 ◽  
Vol 11 (2) ◽  
pp. 103
Author(s):  
Zumrotun Nafisah ◽  
Febrian Rachmadi ◽  
Elly Matul Imah
Keyword(s):  
Face Recognition ◽  
Research Area ◽  
Experimental Results ◽  
Activation Functions ◽  
Backpropagation Algorithm ◽  
Complex Valued ◽  
Better Than

Face recognition is one of biometrical research area that is still interesting. This study discusses the Complex-Valued Backpropagation algorithm for face recognition. Complex-Valued Backpropagation is an algorithm modified from Real-Valued Backpropagation algorithm where the weights and activation functions used are complex. The dataset used in this study consist of 250 images that is classified in 5 classes. The performance of face recognition using Complex-Valued Backpropagation is also compared with Real-Valued Backpropagation algorithm. Experimental results have shown that Complex-Valued Backpropagation performance is better than Real-Valued Backpropagation.

scholarly journals Verifying the Effectiveness of New Face Spoofing DB with Capture Angle and Distance

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 661
Author(s):  
Jin Yeong Bok ◽  
Kun Ha Suh ◽  
Eui Chul Lee
Keyword(s):  
Image Analysis ◽  
Face Recognition ◽  
Three Dimensional ◽  
Image Sensor ◽  
Detection Accuracy ◽  
Lighting Conditions ◽  
Additional Equipment ◽  
Recognition Systems ◽  
Face Spoofing ◽  
Better Than

Face recognition is a representative biometric that can be easily used; however, spoofing attacks threaten the security of face biometric systems by generating fake faces. Thus, it is not advisable to only consider sophisticated spoofing cases, such as three-dimensional masks, because they require additional equipment, thereby increasing the implementation cost. To prevent easy face spoofing attacks through print and display, the two-dimensional (2D) image analysis method using existing face recognition systems is reasonable. Therefore, we proposed a new database called the “pattern recognition-face spoofing advancement database” that can be used to prevent such attacks based on 2D image analysis. To the best of our knowledge, this is the first face spoofing database that considers the changes in both the angle and distance. Therefore, it can be used to train various positional relationships between a face and camera. We conducted various experiments to verify the efficiency of this database. The spoofing detection accuracy of our database using ResNet-18 was found to be 96.75%. The experimental results for various scenarios demonstrated that the spoof detection performances were better for images with pinch angle, near distance images, and replay attacks than those for front images, far distance images, and print attacks, respectively. In the cross-database verification result, the performance when tested with other databases (DBs) after training with our DB was better than the opposite. The results of cross-device verification in terms of camera type showed negligible difference; thus, it was concluded that the type of image sensor does not affect the detection accuracy. Consequently, it was confirmed that the proposed DB that considers various distances, capture angles, lighting conditions, and backgrounds can be used as a training DB to detect spoofing attacks in general face recognition systems.

scholarly journals Celebrity Face Recognition using Deep Learning

2018 ◽  
Vol 12 (2) ◽  
pp. 476 ◽  
Author(s):  
Nur Ateqah Binti Mat Kasim ◽  
Nur Hidayah Binti Abd Rahman ◽  
Zaidah Ibrahim ◽  
Nur Nabilah Abu Mangshor
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Facial Expressions ◽  
Experimental Results ◽  
Learning Technique ◽  
Excellent Accuracy ◽  
Better Than

Face recognition is one of the well studied problems by researchers in computer visions.  Among the challenges of this task are the occurrence of different facial expressions like happy or sad, and different views of the images such as front and side views.  This paper experiments a publicly available dataset that consists of 200,000 images of celebrity faces. Deep Learning technique is gaining its popularity in computer vision and this paper applies this technique for face recognition problem.  One of the techniques under deep learning is Convolutional Neural Network (CNN).  There is also pre-trained CNN models that are AlexNet and GoogLeNet, which produce excellent accuracy results.  The experimental results indicate that AlexNet is better than basic CNN and GoogLeNet for face recognition.

A LOW-COMPUTATION APPROACH FOR HUMAN FACE RECOGNITION

2012 ◽  
Vol 26 (06) ◽  
pp. 1256015 ◽  
Author(s):  
WEI-LI FANG ◽  
YING-KUEI YANG ◽  
JUNG-KUEI PAN
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Face Image ◽  
Experimental Results ◽  
Human Face ◽  
Human Face Recognition ◽  
Computation Cost ◽  
Recognition Algorithms

Several 2DPCA-based face recognition algorithms have been proposed hoping to achieve the goal of improving recognition rate while mostly at the expense of computation cost. In this paper, an approach named SI2DPCA is proposed to not only reduce the computation cost but also increase recognition performance at the same time. The approach divides a whole face image into smaller sub-images to increase the weight of features for better feature extraction. Meanwhile, the computation cost that mainly comes from the heavy and complicated operations against matrices is reduced due to the smaller size of sub-images. The reduced amount of computation has been analyzed and the integrity of sub-images has been discussed thoroughly in the paper. The experiments have been conducted to make comparisons among several better-known approaches and SI2DPCA. The experimental results have demonstrated that the proposed approach works well on reaching the goals of reducing computation cost and improving recognition performance simultaneously.

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