Affective computing study of attention recognition for the 3D guide system

Juang, Li-Hong; Wu, Ming-Ni; Lin, Cian-Huei

doi:10.1049/trit.2020.0068

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…e proposed algorithm achieved 95.97% accuracy as compared to other algorithms. Recently researchers in [15][16][17][18][19][20] have presented different features extraction schemes that can be investigated for developing a robust FR algorithm. e main contribution of this paper is as follows: e rest of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

COMSATS Face: A Dataset of Face Images with Pose Variations, Its Design, and Aspects

Haq

Sethi

Ullah

et al. 2022

Mathematical Problems in Engineering

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Due to the three-dimensional formation and flexibility, a human face may appear different in numerous events. Researchers are developing robust and efficient algorithms for face detection, face recognition, and face expression analysis, causing several difficulties due to face poses, illumination, face expression, head orientation, occlusion, hairstyle, etc. To determine the effectiveness of the algorithms, it needs to be tested using a specific benchmark of face images/databases. Face pose is an important factor that severely reduces the recognition ability. In this paper, two contributions are made: (i) a dataset of face images with multiple poses is introduced. The dataset includes 850 images of 50 individuals under 17 different poses (0°, 5°, 10°, 15°, 20°, 25°, 30°, 35°, 55°, -5°, -10°, -15°, -20°, -25°, -30°, -35°, -55°). These images were captured closed to real-world conditions in the time span of five months in COMSATS University, Abbottabad Campus. Face images included in this dataset can reveal the efficiency and robustness of future face detection and face recognition algorithms. (ii) A comparative analysis of three face recognition algorithms such as PAL, PCA, and LDA is presented based on the proposed face database.

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Section: Introductionmentioning

confidence: 99%

COMSATS Face: A Dataset of Face Images with Pose Variations, Its Design, and Aspects

Haq

Sethi

Ullah

et al. 2022

Mathematical Problems in Engineering

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show abstract

QEST: Quantized and Efficient Scene Text Detector Using Deep Learning

Manjari

Verma

Singal

et al. 2023

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Scene text detection is a complicated and one of the most challenging tasks due to different environmental restrictions, such as illuminations, lighting conditions, tiny and curved texts, and many more. Most of the works on scene text detection have overlooked the primary goal of increasing model accuracy and efficiency, resulting in heavy-weight models that require more processing resources. A novel lightweight model has been developed in this paper to improve the accuracy and efficiency of scene text detection. The proposed model relies on ResNet50 and MobileNetV2 as backbones with quantization used to make the resulting model lightweight. During quantization, the precision has been changed from float32 to float16 and int8 for making the model lightweight. In terms of inference time and Floating-Point Operations Per Second (FLOPS), the proposed method outperforms the state-of-the-art techniques by around 30-100 times. Here, well-known datasets, i.e. ICDAR2015 and ICDAR2019, have been utilized for training and testing to validate the performance of the proposed model. Finally, the findings and discussion indicate that the proposed model is more efficient than the existing schemes.

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Affective computing study of attention recognition for the 3D guide system

Cited by 2 publications

References 29 publications

COMSATS Face: A Dataset of Face Images with Pose Variations, Its Design, and Aspects

COMSATS Face: A Dataset of Face Images with Pose Variations, Its Design, and Aspects

QEST: Quantized and Efficient Scene Text Detector Using Deep Learning

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