Research on Facial Features of Major Chinese Nationalities

2014 ◽  
Vol 687-691 ◽  
pp. 837-840
Author(s):  
Zhi Jie Li ◽  
Xiao Dong Duan ◽  
Cun Rui Wang
Keyword(s):  
Face Recognition ◽  
Measurement Method ◽  
Scientific Basis ◽  
Facial Features ◽  
Geometric Features ◽  
Face Images ◽  
Similarities And Differences

This paper analyze the facial features of 6 main chinese nationalities using measurement method on face images. We select several measurement and calculation indices according to the facial geometric features of each group. It is found that Mongolia, Korean and Han nationalitis are similar in facial features, while Tibetans and Uighurs nationalities have larger differences. Analysis of the similarities and differences among groups can provide a scientific basis for face recognition of multiple nationalities.

scholarly journals Multiple Data-Dependent Kernel Fisher Discriminant Analysis for Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yue Liu ◽  
Yibing Li ◽  
Hong Xie ◽  
Dandan Liu
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Facial Features ◽  
Fisher Criterion ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Face Images ◽  
Kernel Fisher Discriminant Analysis ◽  
Multiple Data ◽  
Novel Algorithm

Kernel Fisher discriminant analysis (KFDA) method has demonstrated its success in extracting facial features for face recognition. Compared to linear techniques, it can better describe the complex and nonlinear variations of face images. However, a single kernel is not always suitable for the applications of face recognition which contain data from multiple, heterogeneous sources, such as face images under huge variations of pose, illumination, and facial expression. To improve the performance of KFDA in face recognition, a novel algorithm named multiple data-dependent kernel Fisher discriminant analysis (MDKFDA) is proposed in this paper. The constructed multiple data-dependent kernel (MDK) is a combination of several base kernels with a data-dependent kernel constraint on their weights. By solving the optimization equation based on Fisher criterion and maximizing the margin criterion, the parameter optimization of data-dependent kernel and multiple base kernels is achieved. Experimental results on the three face databases validate the effectiveness of the proposed algorithm.

A 3D Dynamic Face Recognition Method Based on Computer Vision

2014 ◽  
Vol 556-562 ◽  
pp. 5006-5008 ◽  
Author(s):  
Bo Xia Zeng ◽  
Wen Feng Li
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Traditional Method ◽  
Recognition Algorithm ◽  
Facial Features ◽  
Nonlinear Mapping ◽  
Geometric Features ◽  
Feature Points ◽  
Recognition Method ◽  
2D Images

The non-rigid 3D characters recovery technology for 2D images array is affected by background diversity, motion complexity, data losing and noise of feature points, so the recognition and recovery accuracy of facial features deformation is low. Due to the high error in traditional method, the paper puts forward a 3D facial recognition algorithm based on random images array, which converts the 2D features to 3D by nonlinear mapping, and completes the recognition on foundation of 3D geometric features distance. The experimental results show that the method effectively reduces error and improves recognition effects.

Diagonal Maximum Scatter Difference Analysis and its Application to Face Recognition

2010 ◽  
Vol 20-23 ◽  
pp. 123-128
Author(s):  
Jian Guo Wang ◽  
Tie Jun Zhang
Keyword(s):  
Face Recognition ◽  
Facial Features ◽  
Dimensional Vector ◽  
Human Face ◽  
Human Face Recognition ◽  
Difference Analysis ◽  
One Dimensional ◽  
Face Images ◽  
The One ◽  
Novel Algorithm

Diagonal maximum scatter difference (DiaMSD) method for face recognition is proposed in this paper. This novel algorithm is developed based on two techniques, i.e., maximum scatter difference (MSD) and diagonal face images based projection. The DiaMSD method is not only computationally more efficient but also more accurate than the one dimensional (vector-based) MSD method in extracting the facial features for human face recognition. Extensive experiments are performed to test and evaluate the new algorithm using a subset of the FERET face databases. Experimental results show the effectiveness of the proposed method (DiaMSD).

scholarly journals Kruskal-Wallis-Based Computationally Efficient Feature Selection for Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Sajid Ali Khan ◽  
Ayyaz Hussain ◽  
Abdul Basit ◽  
Sheeraz Akram
Keyword(s):  
Face Recognition ◽  
Recognition Accuracy ◽  
Facial Features ◽  
Face Database ◽  
Computationally Efficient ◽  
Accuracy Rate ◽  
Face Images ◽  
Face Features ◽  
Selection For ◽  
Technological World

Face recognition in today’s technological world, and face recognition applications attain much more importance. Most of the existing work used frontal face images to classify face image. However these techniques fail when applied on real world face images. The proposed technique effectively extracts the prominent facial features. Most of the features are redundant and do not contribute to representing face. In order to eliminate those redundant features, computationally efficient algorithm is used to select the more discriminative face features. Extracted features are then passed to classification step. In the classification step, different classifiers are ensemble to enhance the recognition accuracy rate as single classifier is unable to achieve the high accuracy. Experiments are performed on standard face database images and results are compared with existing techniques.

Class Dependent Face Recognition with 3D Deformable Model

2011 ◽  
Vol 211-212 ◽  
pp. 460-464
Author(s):  
Xiao Ming Bai ◽  
Cheng Zhang Wang
Keyword(s):  
Face Recognition ◽  
Deformable Model ◽  
Facial Features ◽  
Color Information ◽  
Block Diagonal Matrix ◽  
Face Images ◽  
Class Information ◽  
Virtual Face ◽  
Color Face Recognition ◽  
Block Diagonal

Both class information of face subjects and color information of face images are very important cues for recognition. In this paper, a novel class dependent color face recognition approach based on block diagonal discriminant NMF is proposed. The approach employs block diagonal matrix to encode color information of face images simultaneously. Block diagonal constraint is imposed on discriminant NMF algorithm to construct feature extraction approach, which fuses class and color information at the same time into the extracted facial features. To improve learning efficiency of the algorithm, 3D active deformable model is exploited to generate virtual face images. Experimental results on CVL and CMU PIE face databases verify the effectiveness of the proposed approach.

scholarly journals Some Information Geometric Aspects of Cyber Security by Face Recognition

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 878
Author(s):  
C. T. J. Dodson ◽  
John Soldera ◽  
Jacob Scharcanski
Keyword(s):  
Face Recognition ◽  
Cyber Security ◽  
Probability Distributions ◽  
Geodesic Distance ◽  
Recognition Method ◽  
Frequency Distributions ◽  
Face Images ◽  
User Access ◽  
Multidimensional Methods ◽  
Joint Probabilities

Secure user access to devices and datasets is widely enabled by fingerprint or face recognition. Organization of the necessarily large secure digital object datasets, with objects having content that may consist of images, text, video or audio, involves efficient classification and feature retrieval processing. This usually will require multidimensional methods applicable to data that is represented through a family of probability distributions. Then information geometry is an appropriate context in which to provide for such analytic work, whether with maximum likelihood fitted distributions or empirical frequency distributions. The important provision is of a natural geometric measure structure on families of probability distributions by representing them as Riemannian manifolds. Then the distributions are points lying in this geometrical manifold, different features can be identified and dissimilarities computed, so that neighbourhoods of objects nearby a given example object can be constructed. This can reveal clustering and projections onto smaller eigen-subspaces which can make comparisons easier to interpret. Geodesic distances can be used as a natural dissimilarity metric applied over data described by probability distributions. Exploring this property, we propose a new face recognition method which scores dissimilarities between face images by multiplying geodesic distance approximations between 3-variate RGB Gaussians representative of colour face images, and also obtaining joint probabilities. The experimental results show that this new method is more successful in recognition rates than published comparative state-of-the-art methods.

Face recognition in an intelligent door lock with ResNet model based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Suphawimon Phawinee ◽  
Jing-Fang Cai ◽  
Zhe-Yu Guo ◽  
Hao-Ze Zheng ◽  
Guan-Chen Chen
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Low Cost ◽  
Ease Of Use ◽  
Raspberry Pi ◽  
Training Methods ◽  
Graphics Recognition ◽  
Face Images ◽  
The Face ◽  
Door Lock

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

scholarly journals Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1013
Author(s):  
Sayan Maity ◽  
Mohamed Abdel-Mottaleb ◽  
Shihab S. Asfour
Keyword(s):  
Face Recognition ◽  
High Resolution ◽  
Gait Cycle ◽  
Gait Recognition ◽  
Recognition System ◽  
Multimodal Fusion ◽  
Surveillance Video ◽  
Low Resolution ◽  
Model Free ◽  
Face Images

Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.

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.

Adapted discriminative coupled mappings for low-resolution face recognition with one-shot

2021 ◽  
pp. 1-15
Author(s):  
Yongjie Chu ◽  
Touqeer Ahmad ◽  
Lindu Zhao
Keyword(s):  
Face Recognition ◽  
Law Enforcement ◽  
High Resolution ◽  
Domain Adaptation ◽  
Face Image ◽  
Discriminative Learning ◽  
Low Resolution ◽  
Face Images ◽  
World Environment

Low-resolution face recognition with one-shot is a prevalent problem encountered in law enforcement, where it generally requires to recognize the low-resolution face images captured by surveillance cameras with the only one high-resolution profile face image in the database. The problem is very tough because the available samples is quite few and the quality of unknown images is quite low. To effectively address this issue, this paper proposes Adapted Discriminative Coupled Mappings (AdaDCM) approach, which integrates domain adaptation and discriminative learning. To achieve good domain adaptation performance for small size dataset, a new domain adaptation technique called Bidirectional Locality Matching-based Domain Adaptation (BLM-DA) is first developed. Then the proposed AdaDCM is formulated by unifying BLM-DA and discriminative coupled mappings into a single framework. AdaDCM is extensively evaluated on FERET, LFW, and SCface databases, which includes LR face images obtained in constrained, unconstrained, and real-world environment. The promising results on these datasets demonstrate the effectiveness of AdaDCM in LR face recognition with one-shot.

Sign in / Sign up

Export Citation Format

Share Document