Face recognition based on a matching algorithm with recursive calculation of oriented gradient histograms

Vokhmintcev, A. V.; Sochenkov, Ilya; Kuznetsov, V. V.; Tikhonkikh, D. V.

doi:10.1134/s1064562416010178

Cited by 18 publications

(8 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the integral projection, the geometric feature extraction method is used to accurately locate the facial features. The selection of features should ensure the most representative, important, and minimally redundant information [ 23 ], and it is necessary to maintain a certain invariance and adaptability under certain external disturbances. Based on this requirement, a plurality of feature points of the face is located.…”

Section: Research On Face Image Recognition Algorithm Based On Cerebellum-basal Ganglia Mechanismmentioning

confidence: 99%

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

Tang

Shabaz

2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

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.

show abstract

Section: Research On Face Image Recognition Algorithm Based On Cerebellum-basal Ganglia Mechanismmentioning

confidence: 99%

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

Tang

Shabaz

2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…One can observe that the fusion registration method has the best performance. Table 1 Matching accuracy (%) and computational complexity (sec) depending on rotation angle Figure 3 The performance of the algorithms in terms of mean square error To process visual scene characteristics, we use an image-matching algorithm based on the recursive calculation of oriented gradient histograms for several circular sliding windows and a pyramidal image decomposition [20,24]. The algorithm yields initial values for a fusion registration algorithm.…”

Section: Computer Simulationmentioning

confidence: 99%

The New Combined Method of the Generation of a 3d Dense Map of Evironment based on History of Camera Positions and the Robot's Movements

Vokhmintcev¹,

Timchenko²

2020

ACTA POLYTECH HUNG

View full text Add to dashboard Cite

A combined solution is proposed for solving the point-plane variational problem in a closed-form based on fusion visual features and 3d point clouds. An accurate method for reconstructing 3d scene is introduced, and the closed-form solutions of registration task for orthogonal transformation are presented. This method is used to reconstruct a threedimensional model of the environment from a set of images and depth map. The given method is used to match and register point clouds with an arbitrary spatial resolution and an arbitrary scale with respect to each other. The suggested method improves the convergence and accuracy of reconstruction methods for dynamic, bulky scenes.

show abstract

“…It was introduced by Dalal and Triggs [96] to detect the human body by representing the objects based on the distribution of gradient intensities and orientations in spatially distributed regions. Moreover, the HOG provides strong orientation and illumination invariance and it is useful for recognizing objects' texture with deformable shapes [97] and small-scale changes [98]. Therefore, the capability of adapting HOG to be applied in MNDT should be investigated to extract defects' features for defect detection and recognition.…”

Section: Artificial Intelligence Applications In Microwave Ndtmentioning

confidence: 99%

Prospect of Using Artificial Intelligence for Microwave Nondestructive Testing Technique: A Review

2019

View full text Add to dashboard Cite

The development in materials technology has produced stronger, lighter, stiffer, and more durable electrically insulating composites which are replacing metals in many applications. These composites require alternative inspection techniques because the conventional nondestructive testing (NDT) techniques such as thermography, eddy currents, ultrasonic, X-ray and magnetic particles have limitations of inspecting them. Microwave NDT technique employing open-ended rectangular waveguides (OERW) has emerged as a promising approach to detect the defects in both metal and composite materials. Despite its promising results over conventional NDT techniques, OERW microwave NDT technique has shown numerous limitations in terms of poor spatial resolution due to the stand-off distance variations, inspection area irregularities and quantitative estimation in imaging the size of defects. Microwave NDT employing OERW in conjunction with robust artificial intelligence approaches have tremendous potential and viability for evaluating composite structures for the purpose mentioned here. Artificial intelligence techniques with signal processing techniques are highly possible to enhance the efficiency and resolution of microwave NDT technique because the impact of artificial intelligence approaches is proven in various conventional NDT techniques. This paper provides a comprehensive review of NDT techniques as well as the prospect of using artificial intelligence approaches in microwave NDT technique with regards to other conventional NDT techniques. INDEX TERMS Microwave nondestructive testing, open-ended rectangular waveguides, artificial intelligence.

show abstract

Face recognition based on a matching algorithm with recursive calculation of oriented gradient histograms

Cited by 18 publications

References 11 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

The New Combined Method of the Generation of a 3d Dense Map of Evironment based on History of Camera Positions and the Robot's Movements

Prospect of Using Artificial Intelligence for Microwave Nondestructive Testing Technique: A Review

Contact Info

Product

Resources

About