Face expression recognition system based on ripplet transform type II and least square SVM

Kar, Nikunja Bihari; Babu, Korra Sathya; Sangaiah, Arun Kumar; Bakshi, Sambit

doi:10.1007/s11042-017-5485-0

Cited by 46 publications

(21 citation statements)

References 44 publications

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“…In the sub-section, we compare the results of the proposed method with other state-of-the-art results. For a fair comparison, note that the experimental settings in this sub-section follow the introduction listed in [50] and [51]. For the CK+ dataset and the JAFFE dataset, both traditional models and deep learning-based models are considered for comparison, the results are shown in Table 7 and Discussion.…”

Section: Comparison With State-of-the-art Workmentioning

confidence: 99%

Facial Expression Recognition Using Pose-Guided Face Alignment and Discriminative Features Based on Deep Learning

Feng

Wang

2021

IEEE Access

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Face expression recognition is a key technology of robot vision, which can help the robotic understand human emotions. However, interference from the real-world, such as light changes, face occlusion, and pose variation, reduces the recognition rate of the model. To solve above problems, in this paper, a novel deep model is proposed to improve the classification accuracy of facial expressions. The proposed model has the following merits: 1) A pose-guided face alignment method is proposed to reduce the intra-class difference, which can overcome the impact of environmental noise; 2) A hybrid feature representation method is proposed to obtain high-level discriminative facial features that achieves better results in classification networks; 3) A lightweight fusion backbone is designed, which combines the VGG-16 and the ResNet to achieve low-data and low-calculation training. Finally, to evaluate the proposed model, we conduct a series of experiments on four benchmark datasets, including the CK+, the JAFFE, the Oulu-CASIA, and the AR. The results show that the proposed model achieves state-of-the-art recognition rates, that is, 98.9%, 96.8%, 94.5%, and 98.7%, respectively. Comparing with the traditional methods and other advanced deep learning methods, the proposed model can comparable performance in a variety of tasks.

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Section: Comparison With State-of-the-art Workmentioning

confidence: 99%

Facial Expression Recognition Using Pose-Guided Face Alignment and Discriminative Features Based on Deep Learning

Feng

Wang

2021

IEEE Access

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“…Fuzzy set theory is a famous paradigm for image enhancement. Many research scholars employed fuzzy‐based approach to enhance the images commonly used fuzzy‐based approaches are fuzzy sure entropy; fuzzy C‐partition and membership functions [11, 12].…”

Section: Type‐ii Fuzzy Logicmentioning

confidence: 99%

Efficient image classification technique for weather degraded fruit images

Gill¹,

Khehra²

2020

IET image process

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“…By now, no predecessor has ever combined object detection, semantic segmentation and linear fitting in antenna parameter measurement. For the first time, this paper validated a fully automatic antenna parameter measurement method based on instance segmentation [29][30][31], least squares, frame sequence analysis and UAV [32][33][34][35], which enjoys remarkable preciseness, rapid recognition and outstanding performance.…”

Section: Related Workmentioning

confidence: 99%

Mobile Communication Base Station Antenna Measurement Using Unmanned Aerial Vehicle

Zhai

et al. 2019

IEEE Access

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Traditional base station antenna measurement methods conducted with professional worker climbing towers tend to raise safety and inefficiency concerns in practical application. Designed to address the above problems, this paper proposes an intelligent and fully automatic antenna measurement unmanned aerial vehicle (UAV) system for mobile communication base station. Firstly, an antenna database, containing 19,715 images, named UAV-Antenna is constructed by image capturing with the help of UAVs flying around various base stations. Secondly, Mask R-CNN is adopted to train an optimal instance segmentation model on UAV-Antenna. Then, pixel coordinates and threshold are utilized for measuring antenna quantity and separate all antenna data for further measuring. Finally, a least squares method is employed for measuring antenna parameters. Experimental results show that the proposed method can not only satisfy the industry application standards, but also guarantee safety of labors and efficiency of performance.

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Face expression recognition system based on ripplet transform type II and least square SVM

Cited by 46 publications

References 44 publications

Facial Expression Recognition Using Pose-Guided Face Alignment and Discriminative Features Based on Deep Learning

Facial Expression Recognition Using Pose-Guided Face Alignment and Discriminative Features Based on Deep Learning

Efficient image classification technique for weather degraded fruit images

Mobile Communication Base Station Antenna Measurement Using Unmanned Aerial Vehicle

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