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Research on face feature point detection algorithm under computer vision

Dan Li

Dan Li

School of Information Engineering, Shaanxi Polytechnic InstituteXianyang, China
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Li, Dan
 and 
Yanfen Jiang

Yanfen Jiang

Hebei Vocational University of Industry and TechnologyShijiazhuang, China
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Jiang, Yanfen
  
Mar 19, 2025
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Published Online: Mar 19, 2025
Received: Nov 07, 2024
Accepted: Feb 16, 2025
DOI: https://doi.org/10.2478/amns-2025-0503
Keywords
© 2025 Dan Li et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Training block diagram of cascade regression algorithm
Training block diagram of cascade regression algorithm

Figure 2.

Test block diagram of cascade regression algorithm
Test block diagram of cascade regression algorithm

Figure 3.

The training manner of conventional CR
The training manner of conventional CR

Figure 4.

Parallel update method based on mixed Gaussian distribution sampling
Parallel update method based on mixed Gaussian distribution sampling

Figure 5.

Comparison of average relative positioning errors of face feature points
Comparison of average relative positioning errors of face feature points

Figure 6.

Cumulative relative error distribution curve and comparison
Cumulative relative error distribution curve and comparison

Figure 7.

Detection rate of each feature point on LFW face data set
Detection rate of each feature point on LFW face data set

Figure 8.

Pupil precision location map
Pupil precision location map

Face detection module evaluation

Test set Forward propagation time/ms Detection time/ms
AFW 7.39 10.76
PASCAL Faces 7.25 12.28
FDDB 7.48 11.68
CASIA-WebFace 7.27 10.80
WIDER Face 7.42 11.59
MALF 7.36 12.15
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Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other
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