scholarly journals The impact of stylization on face recognition

2020 ◽  
Author(s):  
Nicolas Olivier ◽  
Ludovic Hoyet ◽  
Fabien Danieau ◽  
Ferran Argelaguet ◽  
Quentin Avril ◽  
...  
Keyword(s):  
Face Recognition ◽  
The Impact

An Empirical Study of the Impact of Masks on Face Recognition

2021 ◽  
pp. 108308
Author(s):  
Govind Jeevan ◽  
Geevar C. Zacharias ◽  
Madhu S. Nair ◽  
Jeny Rajan
Keyword(s):  
Face Recognition ◽  
Empirical Study ◽  
The Impact

Photometric Normalization Techniques for Illumination Invariance

2010 ◽  
pp. 279-300 ◽  
Author(s):  
Vitomir Štruc ◽  
Vitomir Štruc ◽  
Nikola Pavešic ◽  
Nikola Pavešic
Keyword(s):  
Face Recognition ◽  
Research Effort ◽  
Histogram Equalization ◽  
Point Of View ◽  
Illumination Variation ◽  
Variation Problem ◽  
Total Variation Model ◽  
The World ◽  
The Impact ◽  
Anisotropic Smoothing

Face recognition technology has come a long way since its beginnings in the previous century. Due to its countless application possibilities, it has attracted the interest of research groups from universities and companies around the world. Thanks to this enormous research effort, the recognition rates achievable with the state-of-the-art face recognition technology are steadily growing, even though some issues still pose major challenges to the technology. Amongst these challenges, coping with illumination-induced appearance variations is one of the biggest and still not satisfactorily solved. A number of techniques have been proposed in the literature to cope with the impact of illumination ranging from simple image enhancement techniques, such as histogram equalization, to more elaborate methods, such as anisotropic smoothing or the logarithmic total variation model. This chapter presents an overview of the most popular and efficient normalization techniques that try to solve the illumination variation problem at the preprocessing level. It assesses the techniques on the YaleB and XM2VTS databases and explores their strengths and weaknesses from the theoretical and implementation point of view.

scholarly journals Template Based Pose and Illumination Invariant Face Recognition

2020 ◽  
Vol 8 (5) ◽  
pp. 3220-3229
Keyword(s):  
Face Recognition ◽  
Gabor Wavelet ◽  
Mathematical Methods ◽  
Query Image ◽  
Illumination Invariant ◽  
Face Images ◽  
The Face ◽  
Similar Images ◽  
Proper Face ◽  
The Impact

This article presents a method “Template based pose and illumination invariant face recognition”. We know that pose and Illumination are important variants where we cannot find proper face images for a given query image. As per the literature, previous methods are also not accurately calculating the pose and Illumination variants of a person face image. So we concentrated on pose and Illumination. Our System firstly calculates the face inclination or the pose of the head of a person with various mathematical methods. Then Our System removes the Illumination from the image using a Gabor phase based illumination invariant extraction strategy. In this strategy, the system normalizes changing light on face images, which can decrease the impact of fluctuating Illumination somewhat. Furthermore, a lot of 2D genuine Gabor wavelet with various orientations is utilized for image change, and numerous Gabor coefficients are consolidated into one entire in thinking about spectrum and phase. Finally, the light invariant is acquired by separating the phase feature from the consolidated coefficients. Then after that, the obtained Pose and illumination invariant images are convolved with Gabor filters to obtain Gabor images. Then templates will be extracted from these Gabor images and one template average is generated. Then similarity measure will be performed between query image template average and database images template averages. Finally the most similar images will be displayed to the user. Exploratory results on PubFig database, Yale B and CMU PIE face databases show that our technique got a critical improvement over other related strategies for face recognition under enormous pose and light variation conditions.

scholarly journals Face Identification using Histogram

2020 ◽  
pp. 670-674
Author(s):  
Tejas Rana
Keyword(s):  
Face Recognition ◽  
Multiple Scales ◽  
Recognition Performance ◽  
Computational Time ◽  
Visual Object ◽  
Feature Descriptor ◽  
Visual Object Recognition ◽  
Local Contrast ◽  
Contrast Normalization ◽  
The Impact

Various experiments or methods can be used for face recognition and detection however two of the main contain an experiment that evaluates the impact of facial landmark localization in the face recognition performance and the second experiment evaluates the impact of extracting the HOG from a regular grid and at multiple scales. We observe the question of feature sets for robust visual object recognition. The Histogram of Oriented Gradients outperform other existing methods like edge and gradient based descriptors. We observe the influence of each stage of the computation on performance, concluding that fine-scale gradients, relatively coarse spatial binning, fine orientation binning and high- quality local contrast normalization in overlapping descriptor patches are all important for good results. Comparative experiments show that though HOG is simple feature descriptor, the proposed HOG feature achieves good results with much lower computational time.

Detection Algorithm Research of Face Pose Based on Stereoscopic Vision and Biological Characteristics

2012 ◽  
Vol 460 ◽  
pp. 30-34
Author(s):  
Peng Xu ◽  
Yuan Men Zhou
Keyword(s):  
Face Recognition ◽  
Three Dimensional ◽  
Detection Algorithm ◽  
Stereoscopic Vision ◽  
Accuracy Rate ◽  
Face Images ◽  
Face Features ◽  
Key Points ◽  
Left And Right ◽  
The Impact

The paper introduces a kind of detection method of face pose based on stereoscopic vision technology, approximately divides head’s deflexion into three plane rotations. By calculating the deflexion angle of three directions, you can determine the face’s pose. This method obtains face images by the left and right video channels, first analyses the similarity of double channels’ images to obtain three-dimensional information of face features’ key points. Then calculates three deflexion angles according to these information, therefore can correspondingly adjust and deform the original image to get standard frontal face image, and provides correction image for the latter face recognition. By this method the impact of pose change to face recognition can be reduced obviously in the earlier stage, so the system’s overall recognition accuracy rate is enhanced effectively.

scholarly journals The impact of early bilingualism on the face recognition processes, escrito por Sonia Kandel et al.

2018 ◽  
Vol 14 (3) ◽  
pp. 240-249
Author(s):  
Isadora Rodrigues De Andrade
Keyword(s):  
Face Recognition ◽  
The Face ◽  
The Impact

Resenha deKANDEL, Sonia; BURFIN, Sabine; MÉARY, David; RUIZ-TADA, Elisa; COSTA, Albert; PASCALIS, Olivier. The Impact of Early Bilingualism on Face Recognition Processes. Front Psychol. 2016; 7: 1080. DOI: http://dx.doi.org/10.3389/fpsyg.2016.01080.   ---DOI: http://dx.doi.org/10.31513/linguistica.2018.v14n3a23005

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