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Article | 01-March-2015

SPARSE REPRESENTATION THEORY AND ITS APPLICATION FOR FACE RECOGNITION

Face recognition aims at endowing computers with the ability to identify different human beings according to their face images. However, recognition rate will decrease sharply when it refers to the non-ideal imaging environments or the incorporation of users, such as illumination, pose, expression variations and so on. Besides, it will be also influence the recognition results when the database is too large or small. Sparse representation based classification for face images has been one of

Yongjiao Wang, Chuan Wang, Lei Liang

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 1, 107–124

Article | 01-December-2014

FACE RECOGNITION BASED ON IMPROVED SUPPORT VECTOR CLUSTERING

Traditional methods for face recognition do not scale well with the number of training sample, which limits the wide applications of related techniques. We propose an improved Support Vector Clustering algorithm to handle the large-scale biometric feature data effectively. We prove theoretically that the proposed algorithm converges to the optimum within any given precision quickly. Compared to related state-of-the-art Support Vector Clustering algorithms, it has the competitive performances on

Yongqing Wang, Xiling Liu

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 4, 1807–1829

Article | 24-April-2018

Face Recognition of the Rhinopithecus Roxellana QinlingensisBased on Improved HOG and Sparse Representation

With the researches on face recognition of Rhinopithecusroxellanaqinlingensis, this thesis comes up with some methods that refining traditional HOG and Sparse Representation in order to improve the efficiency in recognizing golden monkeys. As we know, improved HOG is an optimal way to show partial information of an image. Besides, it can also plays an crucial role in staying stability in both optical and geometric distortion, which means the changes in expressions, postures and angles of golden

Cuan Ying, Shi Yaojie

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 4, 194–198

Research Article | 01-September-2017

DYNAMIC FACE RECOGNITION AND TRACKING SYSTEM USING MACHINE LEARNING IN MATLAB AND BIGDATA

Face Recognition being one of the methods in identifying individuals is getting enhanced at a faster rate. This paper demonstrates the process of detection of faces of the individuals through a live monitoring camera using matlab and also aids in tracking them. The large amount of images being collected at each second is stored in big databases like Hadoop- databases(hbase) or Mongodb as they are known for their higher processing speed. The facial features are extracted from all the images and

P.J Leo Evenss, Jennings Mcenroe .S, A.Prabhu Chakkaravarthy

International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 5, 163–173

Article | 14-October-2020

A Comparative Study of Face Recognition Classification Algorithms

I. INTRODUCTION With the rise of artificial intelligence and machine learning, face recognition technology is widely used in life, such as station security, time and attendance punching, and secure payment [1-3], but different face recognition devices use different algorithms. Therefore, this paper analyzes and compares the commonly used classification algorithms in face recognition. The data set in this paper uses the ORL face data set published by Cambridge University in the United Kingdom

Changyuan Wang, Guang Li, Pengxiang Xue, Qiyou Wu

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 3, 23–29

Research paper | 22-August-2018

Face processing in a case of high functioning autism with developmental prosopagnosia

temporal gyrus (aSTS/MTG). In PK the left aSTS/MTG was hypo-activated in comparison to the control subjects. Additionally, functional connectivity analysis revealed decreased inter-hemispheric connectivity between right and left aSTS/MTG in ‘ASD and DP’ patient during face recognition performance as compared to the control subjects. The lack of activity in the left aSTS/MTG observed in the case of the clinical subject, combined with the behavioral, eye-tracking, and neuropsychological results, suggests

Hanna B. Cygan, Hanna Okuniewska, Katarzyna Jednoróg, Artur Marchewka, Marek Wypych, Anna Nowicka

Acta Neurobiologiae Experimentalis, Volume 78 , ISSUE 2, 114–131

Article | 27-December-2017

FACE DETECTION IN PROFILE VIEWS USING FAST DISCRETE CURVELET TRANSFORM (FDCT) AND SUPPORT VECTOR MACHINE (SVM)

Human face detection is an indispensable component in face processing applications, including automatic face recognition, security surveillance, facial expression recognition, and the like. This paper presents a profile face detection algorithm based on curvelet features, as curvelet transform offers good directional representation and can capture edge information in human face from different angles. First, a simple skin color segmentation scheme based on HSV (Hue – Saturation - Value) and

Bashir Muhammad, Syed Abd Rahman Abu-Bakar

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 1, 108–123

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