A Dynamic Unconstrained Feature Matching Algorithm for Face Recognition

Patil, Ganesh G.; Banyal, Rohitash Kumar

doi:10.12720/jait.11.2.103-108

Cited by 24 publications

(2 citation statements)

References 28 publications

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“…Face detection is a hot topic in computer vision study. It is the basic step of many face applications, such as face recognition [1] and facial expression analysis [2]. Faces usually appear in large visual variations in the cluttered light environment, which makes face detection more difficult.…”

Section: Introductionmentioning

confidence: 99%

PruneFaceDet: Pruning lightweight face detection network by sparsity training

Jiang

Xiong

Tian

et al. 2022

Cognitive Comp and Systems

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Face detection is the basic step of many face analysis tasks. In practice, face detectors usually run on mobile devices with limited memory and computing resources. Therefore, it is important to keep face detectors lightweight. To this end, current methods usually focus on directly designing lightweight detectors. Nevertheless, it is not fully explored whether the resource consumption of these lightweight detectors can be further suppressed without too much sacrifice on accuracy. In this study, we propose to apply the network pruning method to the lightweight face detection network, to further reduce its parameters and floating point operations. To identify the channels of less importance, we perform network training with sparsity regularisation on channel scaling factors of each layer. Then, we remove the connections and corresponding weights with near‐zero scaling factors after sparsity training. We apply the proposed pruning pipeline to a state‐of‐the‐art face detection method, EagleEye, and get a shrunken EagleEye model, which has a reduced number of computing operations and parameters. The shrunken model achieves comparable accuracy as the unpruned model. By using the proposed method, the shrunken EagleEye achieves a 56.3% reduction of parameter size with almost no accuracy loss on the WiderFace dataset.

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Section: Introductionmentioning

confidence: 99%

PruneFaceDet: Pruning lightweight face detection network by sparsity training

Jiang

Xiong

Tian

et al. 2022

Cognitive Comp and Systems

View full text Add to dashboard Cite

show abstract

“…With the rapid development of Artificial Intelligence (AI), computer vision as an interdisciplinary subject has widely been used in advertising [1], [2], product placement [3], [4], remote sensing [5], [6], atmospheric study [7], [8], quality control [9], [10], biometrics [11], and amongst others. Eyes are the main source of capturing external information and information conveyed by vision is most abundant and intuitive.…”

Section: Introductionmentioning

confidence: 99%

Stereo Matching Based on Visual Sensitive Information

Wang

Pathan

Dev

2021

2021 6th International Conference on Image, Vision and Computing (ICIVC)

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The area of computer vision is one of the most discussed topics amongst many scholars, and stereo matching is its most important sub fields. After the parallax map is transformed into a depth map, it can be applied to many intelligent fields. In this paper, a stereo matching algorithm based on visual sensitive information is proposed by using standard images from Middlebury dataset. Aiming at the limitation of traditional stereo matching algorithms regarding the cost window, a cost aggregation algorithm based on the dynamic window is proposed, and the disparity image is optimized by using left and right consistency detection to further reduce the error matching rate. The experimental results show that the proposed algorithm can effectively enhance the stereo matching effect of the image providing significant improvement in accuracy as compared with the classical census algorithm. The proposed model code, dataset, and experimental results are available at https://github.com/WangHewei16/Stereo-Matching.

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Optimized Dynamic Feature Matching for Face Recognition

Patil¹,

Banyal

2021

Techno-Societal 2020

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A Dynamic Unconstrained Feature Matching Algorithm for Face Recognition

Cited by 24 publications

References 28 publications

PruneFaceDet: Pruning lightweight face detection network by sparsity training

PruneFaceDet: Pruning lightweight face detection network by sparsity training

Stereo Matching Based on Visual Sensitive Information

Optimized Dynamic Feature Matching for Face Recognition

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