Face recognition using locality sensitive histograms of oriented gradients

Li, Bin; Huo, Guang

doi:10.1016/j.ijleo.2015.12.032

Cited by 24 publications

(15 citation statements)

References 12 publications

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“…For each m we repeated our face recognition experiments 50 times using the MSR-MSF-VQ algorithm, and calculated the mean of the 50 results. The corresponding experimental graph of our proposed algorithm compared with LSHOG (locality sensitive histograms of oriented gradients) [ 49 ] and HOG [ 10 ] plus different dimension reduction algorithms including PCA [ 1 ], MFA [ 50 ], NPE [ 51 ] and LPP [ 7 ] using the same Yale face database are plotted in Fig 7 . The Y-axis denotes the recognition accuracy and the X-axis shows the number of training samples.…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, Fig 7 also shows that the MSR-MSF-VQ algorithm performs best in most cases with the same training set. This result occurs because—although LSHOG is better than the original HOG (as reported in [ 49 ])—the LSHOG algorithm, which computes a histogram of gradient orientations over the entire face at each pixel location, ignores the interactions between different sub-regions, causing its recognition rate to be below that of the proposed MSR-MSF-VQ algorithm. Therefore, we can conclude that the recognition performance of our proposed algorithm is more robust than that of other methods.…”

Section: Resultsmentioning

confidence: 99%

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Face recognition algorithm using extended vector quantization histogram features

Yan

Lee

et al. 2018

PLoS ONE

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In this paper, we propose a face recognition algorithm based on a combination of vector quantization (VQ) and Markov stationary features (MSF). The VQ algorithm has been shown to be an effective method for generating features; it extracts a codevector histogram as a facial feature representation for face recognition. Still, the VQ histogram features are unable to convey spatial structural information, which to some extent limits their usefulness in discrimination. To alleviate this limitation of VQ histograms, we utilize Markov stationary features (MSF) to extend the VQ histogram-based features so as to add spatial structural information. We demonstrate the effectiveness of our proposed algorithm by achieving recognition results superior to those of several state-of-the-art methods on publicly available face databases.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Face recognition algorithm using extended vector quantization histogram features

Yan

Lee

et al. 2018

PLoS ONE

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show abstract

“…Histogram of Oriented Gradients (HOG) is a very popular texture descriptor since Dalal and Triggs presented it in 2005 [ 44 ]. This method has demonstrated a great performance in multiple fields, such as pedestrian detection [ 44 ] or face recognition [ 45 ]. Another very popular descriptor is Local Binary Pattern (LBP) proposed by Ojala et al [ 46 ] due to their simplicity and high capability to extract the intrinsic features from the textures.…”

Section: Related Researchmentioning

confidence: 99%

Textile Retrieval Based on Image Content from CDC and Webcam Cameras in Indoor Environments

García-Olalla

Alegre

Fernández-Robles

et al. 2018

Sensors

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Textile based image retrieval for indoor environments can be used to retrieve images that contain the same textile, which may indicate that scenes are related. This makes up a useful approach for law enforcement agencies who want to find evidence based on matching between textiles. In this paper, we propose a novel pipeline that allows searching and retrieving textiles that appear in pictures of real scenes. Our approach is based on first obtaining regions containing textiles by using MSER on high pass filtered images of the RGB, HSV and Hue channels of the original photo. To describe the textile regions, we demonstrated that the combination of HOG and HCLOSIB is the best option for our proposal when using the correlation distance to match the query textile patch with the candidate regions. Furthermore, we introduce a new dataset, TextilTube, which comprises a total of 1913 textile regions labelled within 67 classes. We yielded 84.94% of success in the 40 nearest coincidences and 37.44% of precision taking into account just the first coincidence, which outperforms the current deep learning methods evaluated. Experimental results show that this pipeline can be used to set up an effective textile based image retrieval system in indoor environments.

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“…Although, several researchers have achieved significant advances in face recognition system, still this application have many challenges like intra-class variations in illumination, expression, noise, pose and occlusion [4,5]. So, robust and discriminant feature generation have become a problem in face recognition [6]. In addition, the dimensionality of collected human face images is often very high, which leads to high computational complexity and a curse of dimensionality issue [7,8].…”

Section: Introductionmentioning

confidence: 99%

Automatic Face Recognition Using Enhanced Firefly Optimization Algorithm and Deep Belief Network

Prakash¹

2020

IJIES

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Face recognition is an emerging research area in biometric identification systems, which attracted many researchers in pattern recognition and computer vision. Since, the human facial images are high dimensional in nature, so a new optimization algorithm was proposed in this paper to reduce the dimension of facial images. At first, the input facial images were collected from ORL, YALE, and FAce Semantic SEGmentation (FASSEG) databases. Then, feature extraction was then performed by using Local Ternary Pattern (LTP) and Binary Robust Invariant Scalable Key points (BRISK) to extract the features from the face images. In addition, an enhanced fire-fly optimizer was used to reject the irrelevant features or to reduce the dimension of the extracted features. In enhanced fire-fly optimization algorithm, Chi square distance measure was used to find the distance between the fire-flies and also it uses only a limited number of feature values for representing the data that effectively reduces the "curse of dimensionality" concern. Finally, Deep Belief Network (DBN) was used to classify the individual's facial images. The experimental outcome showed that the proposed work improved recognition accuracy up to 7-20% compared to the existing work.

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Face recognition using locality sensitive histograms of oriented gradients

Cited by 24 publications

References 12 publications

Face recognition algorithm using extended vector quantization histogram features

Face recognition algorithm using extended vector quantization histogram features

Textile Retrieval Based on Image Content from CDC and Webcam Cameras in Indoor Environments

Automatic Face Recognition Using Enhanced Firefly Optimization Algorithm and Deep Belief Network

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