Color Face Recognition for Degraded Face Images

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.

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Obtain Method of Quaternion Matrix Orthogonal Eigenvector Set and Its Application in Color Face Recognition

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2008.00121 ◽

2009 ◽

Vol 34 (2) ◽

pp. 121-129 ◽

Cited By ~ 8

Author(s):

Fang-Nian LANG

Keyword(s):

Face Recognition ◽

Quaternion Matrix ◽

Color Face Recognition

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Some Information Geometric Aspects of Cyber Security by Face Recognition

Entropy ◽

10.3390/e23070878 ◽

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.

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189624 ◽

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.

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Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video

Electronics ◽

10.3390/electronics10091013 ◽

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.

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The effect of Color space on discriminating power of Color local texture feature for Color face recognition

2017 International Conference on System Science and Engineering (ICSSE) ◽

10.1109/icsse.2017.8030875 ◽

2017 ◽

Cited By ~ 1

Author(s):

Thanh-Dung Dang

Keyword(s):

Face Recognition ◽

Color Space ◽

Texture Feature ◽

Local Texture ◽

Discriminating Power ◽

Color Face Recognition

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-212454 ◽

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.

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Patch-Based Principal Component Analysis for Face Recognition

Computational Intelligence and Neuroscience ◽

10.1155/2017/5317850 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 10

Author(s):

Tai-Xiang Jiang ◽

Ting-Zhu Huang ◽

Xi-Le Zhao ◽

Tian-Hui Ma

Keyword(s):

Principal Component Analysis ◽

Face Recognition ◽

Nearest Neighbor ◽

Spatial Information ◽

Principal Component ◽

Component Analysis ◽

Two Dimensional ◽

Face Images ◽

Pca Method ◽

Local Spatial Information

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

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