使用深度学习技术优化人脸分类的通用成像

IF 1.1 Q3 INFORMATION SCIENCE & LIBRARY SCIENCE
G. S. Kanth, Sivudu Macherla, B. Laxmikantha, K. Samatha, K. R. Kumar, Bechoo Lal
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引用次数: 0

摘要

生物识别技术是基于人的可辨属性或身体属性来识别人的。在计算机感知和设计认可领域,人脸识别的研究也在不断深入。随着成像传感器学科的不断发展,出现了大量的新问题。多焦点人脸检测的主要问题是如何更精确地发现焦点区域。在面部识别、识别和保护识别方面已经有了一些研究;这里的关键问题仍然是考虑那些在单一框架中具有“不同尺寸”和“不同长宽比”的图像,以避免在人脸方面达到或超过人类水平的准确性,例如人脸图像中的噪声,无视光照条件和姿势比例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generality imaging for optimized face classification using deep learning techniques
Relied on discernible or corporeal attributes, human beings are recognized by employing biometric scheme. In computer perception and design ratification domain, progressive studies are carried out in face recognition. Given the constant development in the discipline of imaging sensor, a legion of rest of the novel problems has occurred. The chief issue remains how to discover focus region more precisely for multi-focus face detection. Several studies have been proliferated in face discernment, spotting, and protection acknowledgment; the key problem remains in this is considering those images into contemplation that had “disparate dimensions” and “disparate aspect ratio” in a singular frame avoiding the progression to attain or surpass human-level accuracy in human facial aspect like noise in face pictures, defying lighting conditions and posture ratio.
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来源期刊
JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES
JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES INFORMATION SCIENCE & LIBRARY SCIENCE-
自引率
21.40%
发文量
88
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