The Excellent Properties of a Dense Grid-Based HOG Feature on Face Recognition Compared to Gabor and LBP

Zheng, Xiao; Tan, Hengliang; Ye, Wenling

doi:10.1109/access.2018.2813395

Cited by 54 publications

(21 citation statements)

References 33 publications

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“…A face recognition method has been presented based on dense grid histograms of oriented gradients (HOG) [5]. In that study, the face image has been divided into many dense grids from which the HOG features have been extracted.…”

Section: Related Workmentioning

confidence: 99%

Identifying Ethnics of People through Face Recognition: A Deep CNN Approach

AlBdairi

Xiao

Alghaili

2020

Scientific Programming

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The interest in face recognition studies has grown rapidly in the last decade. One of the most important problems in face recognition is the identification of ethnics of people. In this study, a new deep learning convolutional neural network is designed to create a new model that can recognize the ethnics of people through their facial features. The new dataset for ethnics of people consists of 3141 images collected from three different nationalities. To the best of our knowledge, this is the first image dataset collected for the ethnics of people and that dataset will be available for the research community. The new model was compared with two state-of-the-art models, VGG and Inception V3, and the validation accuracy was calculated for each convolutional neural network. The generated models have been tested through several images of people, and the results show that the best performance was achieved by our model with a verification accuracy of 96.9%.

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Section: Related Workmentioning

confidence: 99%

Identifying Ethnics of People through Face Recognition: A Deep CNN Approach

AlBdairi

Xiao

Alghaili

2020

Scientific Programming

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“…Feature descriptors are also applied as methods for face recognition. For instance, [Xiang, Tan and Ye 2018] makes a comparison between three famous facial feature descriptors, Histograms of Oriented Gradients (HOG), Gabor and Local Binary Pattern (LBP), where it presents the advantages and disadvantages of using each method.…”

Section: Related Workmentioning

confidence: 99%

A Review and Construction of a Real-time Facial Recognition System

Teixeira¹,

Mafra²,

Rodrigues³

et al. 2020

Anais Do Simpósio Brasileiro De Computação Ubíqua E Pervasiva (SBCUP 2020)

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The evolution of surveillance technologies allows a reduction in human interaction with the process, since most of the monitoring functions performed by an individual can be replaced by detection and recognition techniques in real-time. This paper proposes the development of a surveillance system, which uses these techniques to identify individuals present within the field of view of camera. A combination of the Histogram of Oriented Gradient and Support Vector Machine techniques is applied for face detection, while a Residual Network is used during the stage of recognizing individuals. This shows the possibility of implementing this set of techniques, even in hardware with processing limitations.

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“…Although there were many classifiers used for the classification, such as the Euclidean Distance, the Cosine Distance, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Learning Vector Quantization, and Support Vector Machines [69]. The Minimum Euclidean Distance classifier was considered to be one of the most popular classifiers that could be easily designed [70] and widely used [71,72]. In general, it was used to examine the similarities between objects.…”

Section: Classificationmentioning

confidence: 99%

Face Recognition with Symmetrical Face Training Samples Based on Local Binary Patterns and the Gabor Filter

2019

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In the practical reality of face recognition applications, the human face can have only a limited number of training images. However, it is known that, in general, increasing the number of training images also increases the performance of face recognition systems. In this case, a new set of training samples can be generated from the original samples, using the symmetry property of the face. Although many face recognition methods have been proposed in the literature, a robust face recognition system is still a challenging task. In this paper, recognition performance was improved by using the property of face symmetry. Moreover, the effects of illumination and pose variations were reduced. A Two-Dimensional Discrete Wavelet Transform, based on the Local Binary Pattern, which is a new approach for face recognition using symmetry, has been presented. The method has three main stages, preprocessing, feature extraction, and classification. A Two-Dimensional Discrete Wavelet Transform with Single-Level and Gaussian Low-Pass Filter were used, separately, for preprocessing. The Local Binary Pattern, Gray Level Co-Occurrence Matrix, and the Gabor filter were used for feature extraction, and the Euclidean Distance was used for classification. The proposed method was implemented and evaluated using the Olivetti Research Laboratory (ORL) and Yale datasets. This study also examined the importance of the preprocessing stage in a face recognition system. The experimental results showed that the proposed method had a recognition accuracy of 100%, for both the ORL and Yale datasets, and these recognition rates were higher than the methods in the literature.

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The Excellent Properties of a Dense Grid-Based HOG Feature on Face Recognition Compared to Gabor and LBP

Cited by 54 publications

References 33 publications

Identifying Ethnics of People through Face Recognition: A Deep CNN Approach

Identifying Ethnics of People through Face Recognition: A Deep CNN Approach

A Review and Construction of a Real-time Facial Recognition System

Face Recognition with Symmetrical Face Training Samples Based on Local Binary Patterns and the Gabor Filter

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