Face recognition based on full convolutional neural network based on transfer learning model

Fan, Zhongkui; Guan, Yuanyuan

doi:10.2298/csis200922028f

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Firstly, the foreground area of human action is extracted, and binarization is carried out to obtain the binarization image sequence B(x, y, t). Then, the union set of the binary image sequence is evaluated to obtain the feature graph of MEI [11]. The calculation of MEI is as follows:…”

Section: Related Workmentioning

confidence: 99%

Human action recognition using a depth sequence key-frames based on discriminative collaborative representation classifier for healthcare analytics

Wang

Feng²,

Zheng

2022

ComSIS

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Using deep map sequence to recognize human action is an important research field in computer vision. The traditional deep map-based methods have a lot of redundant information. Therefore, this paper proposes a new deep map sequence feature expression method based on discriminative collaborative representation classifier, which highlights the time sequence of human action features. In this paper, the energy field is established according to the shape and action characteristics of human body to obtain the energy information of human body. Then the energy information is projected onto three orthogonal axes to obtain deep spatial temporal energy map. Meanwhile, in order to solve the problem of high misclassification probability of similar samples by collaborative representation classifier (CRC), a discriminative CRC (DCRC) is proposed. The classifier takes into account the influence of all training samples and each kind of samples on the collaborative representation coefficient, it obtains the highly discriminative collaborative representation coefficient, and improves the discriminability of similar samples. Experimental results on MSR Action3D data set show that the redundancy of key-frame algorithm is reduced, and the operation efficiency of each algorithm is improved by 20%-30%. The proposed algorithm in this paper reduces the redundant information in deep map sequence and improves the extraction rate of feature map. It not only preserves the spatial information of human action through the energy field, but also records the temporal information of human action in a complete way. What?s more, it still maintains a high recognition accuracy in the action data with temporal information.

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Section: Related Workmentioning

confidence: 99%

Human action recognition using a depth sequence key-frames based on discriminative collaborative representation classifier for healthcare analytics

Wang

Feng²,

Zheng

2022

ComSIS

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An efficient face recognition attack method based on generative adversarial networks and cosine metrics

Ding

et al. 2021

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)

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A Fast Scenario Transfer Approach for Portrait Styles Through Collaborative Awareness of Convolutional Neural Network and Generative Adversarial Learning

Wang,

Liang

2023

J CIRCUIT SYST COMP

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The fast scenario transfer for portrait styles has been a universal concern in the area of image processing. Conventionally, it was realized mostly by manual manipulation based on expertise experience, which costs a large amount of human labor. To fit the growing business volume in the era of Internet 4.0, it is supposed to investigate reliable autonomous workflow that generates portrait-style results via intelligent algorithms. Therefore, this paper proposes a fast scenario transfer method for portrait styles through collaborative awareness of convolutional neural network (CNN) and generative learning (GAL). On the one hand, a CNN structure is designed to make feature extraction towards the initial images, which can be used for following image generation. On the other hand, the GAL is utilized to generate new images with post-transferred scenario styles, so that the expected scenario style transfer results can be output by generating new images through long-term iterations. Especially, the loss function is designed by simultaneously considering local and global characteristics. Finally, the analysis and experimental results show that the correction process can be completed by injecting the extracted input face features into the feature maps of corresponding sizes in the generation process using a pre-trained feature extractor. Thus, the face conversion efficiency can be improved.

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Face recognition based on full convolutional neural network based on transfer learning model

Cited by 3 publications

References 16 publications

Human action recognition using a depth sequence key-frames based on discriminative collaborative representation classifier for healthcare analytics

Human action recognition using a depth sequence key-frames based on discriminative collaborative representation classifier for healthcare analytics

An efficient face recognition attack method based on generative adversarial networks and cosine metrics

A Fast Scenario Transfer Approach for Portrait Styles Through Collaborative Awareness of Convolutional Neural Network and Generative Adversarial Learning

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