Efficient color face recognition based on quaternion discrete orthogonal moments neural networks

Alami, Abdelmajid El; Berrahou, Nadia; Lakhili, Zouhir; Mesbah, Abderrahim; Berrahou, Aissam; Qjidaa, Hassan

doi:10.1007/s11042-021-11669-3

Cited by 10 publications

(4 citation statements)

References 48 publications

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“…Moreover, the quaternion-weighted spherical Bessel-Fourier moment (QSBFM) was proposed in [82] and the application of color image reconstruction and object recognition in the CVG-UGR dataset (http://decsai.ugr .es/cvg/dbimagenes/index.php, accessed on 1 March 2023), Amsterdam Library (https: //ccia.ugr.es/cvg/dbimagenes/index.php, accessed on 1 March 2023), and Columbia Library (https://www.cs.columbia.edu/CAVE/software/softlib/coil-100.php, accessed on 1 March 2023) illustrated the effectiveness of the quaternion representation. In [83], the discrete orthogonal moment was applied to neural networks for color face recognition. Other moment-based models, such as the quaternion Fourier-Mellin moment [84] and the quaternion radial moment [85], demonstrated the competitiveness of quaternion-based models.…”

Section: Moment-based Modelsmentioning

confidence: 99%

“…Later, they proposed a singleimage dehazing model based on quaternion neural networks [126]. EI et al [83] [127] designed a non-iterative quaternion routing algorithm to integrate quaternion-valued capsule networks. Xu et al [128] proposed a plug-and-play model for image denoising and inpainting by combing the FFDNet [129] and low-rank (Laplace) function in the quaternion domain.…”

Section: Deep Learningmentioning

confidence: 99%

See 1 more Smart Citation

Review of Quaternion-Based Color Image Processing Methods

Huang

Gao

2023

Mathematics

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Images are a convenient way for humans to obtain information and knowledge, but they are often destroyed throughout the collection or distribution process. Therefore, image processing evolves as the need arises, and color image processing is a broad and active field. A color image includes three distinct but closely related channels (red, green, and blue (RGB)). Compared to directly expressing color images as vectors or matrices, the quaternion representation offers an effective alternative. There are several papers and works on this subject, as well as numerous definitions, hypotheses, and methodologies. Our observations indicate that the quaternion representation method is effective, and models and methods based on it have rapidly developed. Hence, the purpose of this paper is to review and categorize past methods, as well as study their efficacy and computational examples. We hope that this research will be helpful to academics interested in quaternion representation.

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Section: Moment-based Modelsmentioning

confidence: 99%

Section: Deep Learningmentioning

confidence: 99%

Review of Quaternion-Based Color Image Processing Methods

Huang

Gao

2023

Mathematics

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“…In face recognition, the forward propagation calculation formula of the deep learning model CNN structure is shown in Eq. (11).…”

Section: B Research On Multi-task Face Recognition Algorithm Based On...mentioning

confidence: 99%

“…In Eq. (11), o represents the forward propagation training value; n W represents the n weight of n b the first n layer; represents the displacement bias of the first layer; and () g represents the final classification function. The ultimate goal of network training is to obtain the minimized loss function value loss, so the Softmax-loss function is selected to calculate the loss rate, and its specific formula is shown in Eq.…”

Section: B Research On Multi-task Face Recognition Algorithm Based On...mentioning

confidence: 99%

Research on Image Algorithm for Face Recognition Based on Deep Learning

2023

IJACSA

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As people's requirements for applications are getting higher and higher, the recognition of facial features has been paid more and more attention. The current facial feature recognition algorithm not only takes a long time, but also has problems such as large system resource consumption and long running time in practical applications. Based on this, the research proposes a multi-task face recognition algorithm by combining multi-task deep learning on the basis of convolutional neural network, and analyzes its performance in four dimensions of face identity, age, gender, and fatigue state. The experimental results show that the multi-task face recognition algorithm model obtained through layer-by-layer progression takes less time than other models and can complete more tasks in the same training time. At the same time, comparing the best model M44 with other algorithms in four dimensions, it is found that the Mean Absolute Error lowest is 3.53, and the highest Accuracy value is 98.3%. On the whole, the multi-task face recognition algorithm proposed in the study can recognize facial features efficiently and quickly. At the same time, its training time is short, the calculation speed is fast, and the recognition accuracy is much higher than other algorithms. It is applied to intelligent driving behavior. Analysis, intelligent clothing navigation and other aspects have strong practical significance.

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