Face recognition method based on HOG and DMMA from single training sample

Ji, Qingbo; Enze, Zheng; Xinqi, Yue; Xie, Ying; Lin, Yun

doi:10.1007/s11042-015-3005-7

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…Basketball is an important part of the physical education curriculum in ordinary colleges and universities. Research on how to quickly improve the basketball skills of college students has a demonstrative effect on the development of college students' sports skills [ 4 ]. However, this research hopes to study the impact of college students' physical fitness on basketball skills through structural equation modeling.…”

Section: Introductionmentioning

confidence: 99%

Research on Cardiorespiratory Endurance Test and Training Method Based on Structural Equation Model

Wang

2022

Journal of Healthcare Engineering

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In order to study the cardiorespiratory endurance test and training method under the structural equation model, a structural equation model built in conjunction with ANOS7.0 is proposed. This method uses confirmatory factor analysis and analyzes the influencing factors of motor skills. Studies indicate that df = 173 > 0 of the structural equation model, to over-identify the model, the absolute value of the standardized coefficient value is 0.257 to 0.758, less than 0.95, and there is no value with a large estimated standard deviation. The model did not violate the estimation phenomenon. Among the factors affecting motor skills, cardiopulmonary function, and muscle endurance, the weight of the back muscles is less than 0.4, the power of coordination and explosive power of lower limbs is greater than 0.6, and limb muscle strength, body mass, and the stretch and flexibility of the muscles have a greater weight on basketball skills than 0.5. The structural equation model is an over-recognition model. The absolute value of the standardized coefficient value does not have a value with an excessively large standard deviation estimate, and the model does not violate the estimate.

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Section: Introductionmentioning

confidence: 99%

Research on Cardiorespiratory Endurance Test and Training Method Based on Structural Equation Model

Wang

2022

Journal of Healthcare Engineering

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“…This technique does not consider the local features of the face image. Qingbo et al (2016) took the advantage by the fusion of two sets of features in their proposal. HOG was used to extract one set of features, and discriminative multi-manifold analysis algorithm was used to extract another feature set.…”

Section: Motivationmentioning

confidence: 99%

“…The features from face images can be effectively described by histograms of oriented gradients-based (HOG-based) (Qingbo et al , 2016) and co-occurrence HOG-based (Co-HOG-based) (Pang et al , 2012) methods. The HOG allows feature extraction through the distribution of the gradients of the images among various bins by a decomposition process.…”

Section: Introductionmentioning

confidence: 99%

Hybrid feature descriptor and probabilistic neuro-fuzzy system for face recognition

Bindu

Manjunathachari

2018

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Purpose This paper aims to develop the Hybrid feature descriptor and probabilistic neuro-fuzzy system for attaining the high accuracy in face recognition system. In recent days, facial recognition (FR) systems play a vital part in several applications such as surveillance, access control and image understanding. Accordingly, various face recognition methods have been developed in the literature, but the applicability of these algorithms is restricted because of unsatisfied accuracy. So, the improvement of face recognition is significantly important for the current trend. Design/methodology/approach This paper proposes a face recognition system through feature extraction and classification. The proposed model extracts the local and the global feature of the image. The local features of the image are extracted using the kernel based scale invariant feature transform (K-SIFT) model and the global features are extracted using the proposed m-Co-HOG model. (Co-HOG: co-occurrence histograms of oriented gradients) The proposed m-Co-HOG model has the properties of the Co-HOG algorithm. The feature vector database contains combined local and the global feature vectors derived using the K-SIFT model and the proposed m-Co-HOG algorithm. This paper proposes a probabilistic neuro-fuzzy classifier system for the finding the identity of the person from the extracted feature vector database. Findings The face images required for the simulation of the proposed work are taken from the CVL database. The simulation considers a total of 114 persons form the CVL database. From the results, it is evident that the proposed model has outperformed the existing models with an improved accuracy of 0.98. The false acceptance rate (FAR) and false rejection rate (FRR) values of the proposed model have a low value of 0.01. Originality/value This paper proposes a face recognition system with proposed m-Co-HOG vector and the hybrid neuro-fuzzy classifier. Feature extraction was based on the proposed m-Co-HOG vector for extracting the global features and the existing K-SIFT model for extracting the local features from the face images. The proposed m-Co-HOG vector utilizes the existing Co-HOG model for feature extraction, along with a new color gradient decomposition method. The major advantage of the proposed m-Co-HOG vector is that it utilizes the color features of the image along with other features during the histogram operation.

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“…Computer vision based on deep learning technology can also have a profound impact on other disciplines [4], such as animation simulation and real-time rendering technology in computer graphics, microscopic image analysis technology in the field of materials, medical image analysis and processing technology, smart education for real-time assessment of teachers' and students' classroom performance and examination room behaviour, intelligent system for analysing athletes' performance and technical statistics, and so on. Object detection algorithms have shifted from traditional algorithms such as HOG [5], SIFT [6] and LBP [7] based on handcrafted features to machine learning techniques based on deep neural networks. Among these, the target detection technology based on deep learning consists of RCNN [8], Fast RCNN [9] and other technologies.…”

Section: Introductionmentioning

confidence: 99%

Research on computer vision technology based on BP-LSTM hybrid network

Yi¹,

Ling²,

Chen³

et al. 2023

Applied Mathematics and Nonlinear Sciences

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The computer vision direction in the field of artificial intelligence analyses the latest progress of computer vision technology from visual perception and visual generation, including but not limited to image recognition, target detection and image segmentation. First of all, for computer vision technology, this paper introduces the detailed application of image recognition technology, object detection technology and image segmentation technology. Then, we build a BP neural network combined with a deep LSTM neural network, use the BP network algorithm to select the input variables to reduce the dimension and complexity of the model, and use the selected variables as the input of the deep LSTM network. At the same time, deep LSTM is used to perform high-dimensional deep memory learning features on the selected variables. Finally, the model is separately experimented in computer vision. The experimental results show that the present model and other single models can be selected by BP neural network variables in computer vision applications, which can effectively reduce the complexity of the model and improve the generalisation ability of the model, so that it can be used in computer vision research.

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Face recognition method based on HOG and DMMA from single training sample

Cited by 7 publications

References 22 publications

Research on Cardiorespiratory Endurance Test and Training Method Based on Structural Equation Model

Research on Cardiorespiratory Endurance Test and Training Method Based on Structural Equation Model

Hybrid feature descriptor and probabilistic neuro-fuzzy system for face recognition

Research on computer vision technology based on BP-LSTM hybrid network

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