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The Influence of Dynamical Downscaling and Boundary Layer Selection on Egypt’s Potential Evapotranspiration using a Calibrated Version of the Hargreaves-samani Equation: RegCM4 Approach
Samy A Anwar and Ankur Srivastava
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Mars Ascent Propellants and Life Support Resources - Take it or Make it?
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Medicine Group Review Article Article ID: igmin231

A Study of Multi-Pose Effects On a Face Recognition System

Forensic Medicine DOI10.61927/igmin231
Yichao Cao *
Affiliation

Affiliation

    Yichao Cao, Shanghai Research Institute of Criminal Science and Technology, Shanghai Key Laboratory of Crime Scene Evidence, Shanghai, PR China, Email: [email protected]

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Abstract

Interpersonal and intrapersonal face variation interference caused by multiple poses is challenging for distance-based face recognition systems. In this paper, we investigate the face-feature distance distribution for Chinese multi-pose faces. The simulation shows that the number of individuals in the gallery database will greatly affect the recognition performance for near-profile face images. It also provides a prediction of the Top-N occurrence rates in different gallery-size environments.

Figures

Multi-pose feature distance variation for the same individual in part I of the dataset. Figure 1: Multi-pose feature distance variation for the same...
Multi-pose feature distance variation for the same individual in part II of the dataset. Figure 2: Multi-pose feature distance variation for the same...
Multi-pose feature distance variation for different individuals in part II of the dataset. Figure 3: Multi-pose feature distance variation for differen...
Interpersonal and intrapersonal multi-pose distance variation. Figure 4: Interpersonal and intrapersonal multi-pose distanc...
Probability plot for intrapersonal multi-pose distance data and 2 fitted distributions. Figure 5: Probability plot for intrapersonal multi-pose dist...
Occurrence rate at pose PD67 after 10k test runs. Figure 6: Occurrence rate at pose PD67 after 10k test runs....

References

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    2. Guan Y, Fang J, Wu X. Multi-pose face recognition using Cascade Alignment Network and incremental clustering. Signal Image Video Process. 2021;15(1):63-71. doi:10.1007/s11760-020-01832-4.
    3. He H, Liang J, Hou LX, Yunfei L. Multi-pose face reconstruction and Gabor-based dictionary learning for face recognition. Appl Intell. 2023;53(13):16648-16662. doi:10.1007/s10489-023-02954-1.
    4. Tu X, Zhao J, Liu Q, WenjieGuo, GuodongLi, ZhifengLiu, WeiFeng, Jiashi. Joint Face Image Restoration and Frontalization for Recognition. IEEE Trans Circuits Syst Video Technol. 2022;32(3):1285-1298. doi:10.1109/TCSVT.2021.3077655.
    5. Ding C, Xu C, Tao D. Multi-Task Pose-Invariant Face Recognition. IEEE Trans Image Process. 2015;24(3):980-993. doi:10.1109/TIP.2014.2383290.
    6. Luan X, Geng H, Liu L, Li W, Ren M. Geometry Structure Preserving based GAN for Multi-Pose Face Frontalization and Recognition. IEEE Access. 2020;PP(99):1-1. doi:10.1109/ACCESS.2020.2997572.
    7. Zeng K, Wang Z, Han CZ. Implicit space pose consistent transfer network for deep face verification. Pattern Recognit Lett. 2023;176:1-6. doi:10.1016/j.patrec.2022.12.009.
    8. Philbin FSDKJ. FaceNet: A unified embedding for face recognition and clustering. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, USA; 2015:815-823. doi:10.1109/CVPR.2015.7298682.
    9. Wen G, et al. The CAS-PEAL large-scale Chinese face database and baseline evaluations. IEEE Trans Syst Man Cybern. 2008;38(1):149-161. doi:10.1109/TSMCB.2007.906173.
    10. Limin B, Mingxing J. A Facial Point Detection and Face Recognition Algorithm Based on Multi-pose (in chi). Comput Digit Eng. 2018;46(7):1440-1445,1457. doi:10.3969/j.issn.1672-9722.2018.07.035.
    11. Phillips PJ, Martin A, Wilson CL, Przybocki M. An introduction evaluating biometric systems. Comput. 2000;33(2):56-63. doi:10.1109/2.826988.
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