Learning Discriminant Face Descriptor for Face Recognition

2013 ◽  
pp. 748-759 ◽  
Author(s):  
Zhen Lei ◽  
Stan Z. Li
Keyword(s):  
Face Recognition ◽  
Face Descriptor

scholarly journals A New Local Descriptor Based on Strings for Face Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Hicham Zaaraoui ◽  
Abderrahim Saaidi ◽  
Rachid El Alami ◽  
Mustapha Abarkan
Keyword(s):  
Face Recognition ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Levenshtein Distance ◽  
Local Descriptor ◽  
Chain Code ◽  
Visual Words ◽  
The Face ◽  
Face Descriptor ◽  
Neighbor Classifier

This paper proposes the use of strings as a new local descriptor for face recognition. The face image is first divided into nonoverlapping subregions from which the strings (words) are extracted using the principle of chain code algorithm and assigned into the nearest words in a dictionary of visual words (DoVW) with the Levenshtein distance (LD) by applying the bag of visual words (BoVW) paradigm. As a result, each region is represented by a histogram of dictionary words. The histograms are then assembled as a face descriptor. Our methodology depends on the path pursued from a starting pixel and do not require a model as the other approaches from the literature. Therefore, the information of the local and global properties of an object is obtained. The recognition is performed by using the nearest neighbor classifier with the Hellinger distance (HD) as a comparison between feature vectors. The experimental results on the ORL and Yale databases demonstrate the efficiency of the proposed approach in terms of preserving information and recognition rate compared to the existing face recognition methods.

scholarly journals Hybrid Technique on Image Clustering

2020 ◽  
Vol 9 (6) ◽  
pp. 354-358
Keyword(s):  
Face Recognition ◽  
Facial Expression ◽  
Recognition Rate ◽  
Image Clustering ◽  
Hybrid Technique ◽  
Global Features ◽  
Good Efficiency ◽  
Face Clustering ◽  
Main Motive ◽  
Face Descriptor

Face recognition using FLD for extracting high dimensional images is introduced in this paper. The main purpose is to work on removing bugs and noise from the images and extract the facial expression applied on face descriptor. FLD is selected for increasing the discrimination information [17]. The main points of this paper give the brief knowledge about the face recognition and face clustering. Its shows how biometric terms help the local and global features for extracting information from database. Finding better solutions to deal with noise in face recognition is a challenging task [18]. We also performed some comparative analysis on various face recognition techniques. The main motive of this paper is to increase the recognition rate of the images and provide good efficiency. This method defines how the features and facial expression are extracted and all noise and bugs are eliminated to make a separate individual cluster of same known faces.

scholarly journals Real Time Face Recognition Based on Face Descriptor and Its Application

2018 ◽  
Vol 16 (2) ◽  
pp. 739
Author(s):  
I Gede Pasek Suta Wijaya ◽  
Ario Yudo Husodo ◽  
I Wayan Agus Arimbawa
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Face Descriptor

Gabor-scale binary pattern for face recognition

2016 ◽  
Vol 14 (05) ◽  
pp. 1650035 ◽  
Author(s):  
Zhi-Ming Li ◽  
Zheng-Hai Huang ◽  
Ting Zhang
Keyword(s):  
Face Recognition ◽  
Wavelet Transform ◽  
Spatial Frequency ◽  
Local Binary Pattern ◽  
Experimental Results ◽  
Gabor Wavelet ◽  
Special Orientation ◽  
Scale Ratio ◽  
Face Descriptor ◽  
Scale Length

In this paper, a novel face descriptor, the Gabor-scale binary pattern (GSBP), is proposed to explore the neighboring relationship in spatial, frequency and orientation domains for the purpose of face recognition. In order to extract the GSBP feature, the Gabor-scale volume and the Gabor-scale vector are introduced by using a group of Gabor wavelet coefficients with a special orientation. Moreover, the Gabor-scale length pattern and the Gabor-scale ratio pattern are proposed. Compared with the existed methods, GSBP utilizes the deep relations between neighboring Gabor subimages instead of directly combining Gabor wavelet transform and local binary pattern. For estimating the performance of GSBP, we compare the proposed method with the related methods on several popular face databases, including LFW, FERET, AR, Yale and Extended YaleB databases. The experimental results show that the proposed method outperforms several popular face recognition methods.

scholarly journals Face Biometrics as a Potential Predictor for COVID-19 Susceptibility

2021 ◽  
Author(s):  
Oleg Kit
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Disease Severity ◽  
Virus Disease ◽  
Binary Classification ◽  
Severe Form ◽  
K Nearest Neighbors ◽  
Face Descriptor ◽  
The Individual

Fighting against the COVID-19 pandemic caused by the SARS-CoV-2 virus is one of the most critical challenges facing the global health system today. The possibility to identify the group of persons in the cohort of people under 50 years old, who are sensitive to the COVID-disease by non-invasive methods, is a very perspective approach for estimating the epidemiological state of the human population. The study aimed to identify the features of people's faces with COVID-19 that the most correlate with disease severity could serve as one of these approaches. For this aim, 525 photos of patients' faces with different outcomes of COVID-19 disease were analyzed using the Dlib face recognition convolutional neural network pre-trained for face recognition. Face descriptor vectors were obtained using the convolutional neural network. Facial features were found that predict a person's sensitivity to the SARS-CoV-2 virus (disease severity), and the contribution of each of the features to the risk of developing a severe form of COVID in a person was found. The accuracy of the binary classification of the individual severity of the COVID-19 course using the k-nearest neighbors algorithm on the test dataset was accuracy - 84%, AUC - 0.90.

Learning Compact Binary Face Descriptor for Face Recognition

2015 ◽  
Vol 37 (10) ◽  
pp. 2041-2056 ◽  
Author(s):  
Jiwen Lu ◽  
Venice Erin Liong ◽  
Xiuzhuang Zhou ◽  
Jie Zhou
Keyword(s):  
Face Recognition ◽  
Compact Binary ◽  
Face Descriptor
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