An efficient face recognition method using contourlet and curvelet transform

Biswas, Suparna; Sil, Jaya

doi:10.1016/j.jksuci.2017.10.010

Cited by 28 publications

(18 citation statements)

References 20 publications

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“…A dimensional reduction of space per function was proposed according to the entropy of the conversion coefficients. Selected features were used to recognize the face images using a support vector machine (SVM) classifier [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

A New Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism

Tang

Shabaz

2021

Journal of Healthcare Engineering

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Face recognition is one of the popular areas of research in the field of computer vision. It is mainly used for identification and security system. One of the major challenges in face recognition is identification under numerous illumination environments by changing the direction of light or modifying the lighting magnitude. Exacting illumination invariant features is an effective approach to solve this problem. Conventional face recognition algorithms based on nonsubsampled contourlet transform (NSCT) and bionic mode are not capable enough to recognize the similar faces with great accuracy. Hence, in this paper, an attempt is made to propose an enhanced cerebellum-basal ganglia mechanism (CBGM) for face recognition. The integral projection and geometric feature assortment method are used to acquire the facial image features. The cognition model is deployed which is based on the cerebellum-basal ganglia mechanism and is applied for extraction of features from the face image to achieve greater accuracy for recognition of face images. The experimental results reveal that the enhanced CBGM algorithm can effectively recognize face images with greater accuracy. The recognition rate of 100 AR face images has been found to be 96.9%. The high recognition accuracy rate has been achieved by the proposed CBGM technique.

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Section: Introductionmentioning

confidence: 99%

A New Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism

Tang

Shabaz

2021

Journal of Healthcare Engineering

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“…The framework consists of a facial detection and recognition tracking application module, and a data analysis cloud storage module. Biswas and Sil [36] proposed a method for face recognition using contourlet transform (CNT) and curvelet transform (CLT) which improves the rate of face recognition under different challenges. Cheng et al [37] proposed an effective illumination estimation model based on Lambertian reflectance to extract illumination invariants for face recognition under complex illumination conditions.…”

Section: Global Approachmentioning

confidence: 99%

Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

Moung¹

2020

IJATCSE

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In the past decade, face recognition has gained an important role among the most frequently used image processing applications and the availability of viable technologies in this field has also contributed significantly to this. Face recognition has become an enabler in healthcare, surveillance, photo cataloging, attendance, and much more, which will be discussed in this review paper. Despite rapid progress in face-recognition technology, various challenges such as variations, occlusion, facial expressions, aging and many more that affect the performance of the system still need to be addressed. This paper presents a review on the state-of-the-art, enablers, challenges and solutions for face recognition. Face recognition can be categorized into three groups; namely global approach, local feature approach, and hybrid approach. The global approach uses the whole face as input for the face recognition system. The local approach uses measurements between important landmarks of a face and certain face region selection for training. The hybrid approach blends global and local approaches in which the hybrid approach uses the best global and local approach methods. The challenges of face recognition are; (i) automated face detection where difficulties lies on detecting a person's face, (ii) pose variations cause by rotation of people's head, (iii) face occlusion, (iv) facial expression changes, (v) ageing of the face, (vi) varying illumination conditions, (vii) low image resolution, (viii) identity look-alike or twin, and (ix) other technical difficulties. Finally, the solutions to each of the highlighted challenges were described. The survey found that all the images considered for training and testing were made up of RGB images. With the rapid growth of computer technology in terms of computing speed and the increasingly sophisticated functions of smartphones, multispectral or even hyperspectral imagery could be considered for face-recognition research.

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“…S. Biswas and J. Sil, [18] developed a new method for FR to increase the recognition rate specifically Contourlet Transform (CNT) and Curvelet Transform (CLT) methods. The proposed method provided two advantages such as (i) extract the highly correlated information on statistical features of different directional sub bands by CNT.…”

Section: Literature Reviewmentioning

confidence: 99%

Grey Wolf Optimizer with Linear Collaborative Discriminant Regression Classification based Face Recognition

Hosgurmath¹,

Mallappa²

2019

IJIES

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For the past few decades, biometric Face Recognition (FR) is the active research area in different domains such as image processing, pattern recognition, etc. The existing FR system has several limitations such as Single Sample Problem, maximum Reconstruction Errors (REs), these problems decreases the FR rate. In this research paper, an efficient FR method is proposed, namely Grey Wolf Optimizer based Linear Collaborative Discriminant Regression Classification (GWO-LCDRC). The optimization technique of GWO algorithm is applied in LCDRC to select the relevant weight value in LCDRC. For every training sample, optimal weight values are selected to improve the recognition rate. The proposed GWO-LCDRC method maximize the collaboration of Between Class RE (BCRE) and minimize the Within Class RE (WCRE). An experimental analysis conducted on the two standard facial databases namely ORL and YALE. The proposed GWO-LCDRC method improved the performance of recognition accuracy with respect to different training samples. Also, the performance is compared with the traditional methods Linear Regression Classification (LRC), Linear Discriminant Regression Classification (LDRC), and LCDRC. The overall experiment demonstrated that the proposed GWO-LCDRC method achieved approximately 3% and 6.5% of FR accuracy improvement with respect to ORL and YALE respectively.

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An efficient face recognition method using contourlet and curvelet transform

Cited by 28 publications

References 20 publications

A New Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism

A New Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism

Face Recognition State-of-the-art, Enablers, Challenges and Solutions: A Review

Grey Wolf Optimizer with Linear Collaborative Discriminant Regression Classification based Face Recognition

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